{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uGV2VjXF4pNs"
      },
      "source": [
        "# 查看FashionMNIST原始数据格式"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "ExecuteTime": {
          "end_time": "2025-06-26T01:43:32.363026Z",
          "start_time": "2025-06-26T01:43:29.447990Z"
        },
        "id": "3djTfPq64pNt"
      },
      "outputs": [],
      "source": [
        "import torch\n",
        "import torchvision\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "from torchvision import datasets, transforms\n",
        "from deeplearning_func import EarlyStopping, ModelSaver,train_classification_model,plot_learning_curves\n",
        "from deeplearning_func import evaluate_classification_model as evaluate_model\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "id": "Fi46_oyAY6qD"
      },
      "outputs": [],
      "source": [
        "import json\n",
        "token = {\"username\":\"cskaoyan\",\"key\":\"ff99d9d7ff71704e3e761217ceec03c5\"}\n",
        "with open('/content/kaggle.json', 'w') as file:\n",
        "  json.dump(token, file)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FBunE0OvY6ZY",
        "outputId": "09d57423-6ce3-4dff-9c25-5c80bf7ab415"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "{\"username\": \"cskaoyan\", \"key\": \"ff99d9d7ff71704e3e761217ceec03c5\"}"
          ]
        }
      ],
      "source": [
        "!cat /content/kaggle.json"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qXgB8rdbZIDU",
        "outputId": "a3fb443d-b3fc-474b-91c2-3fdc48797e58"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "- path is now set to: /content\n"
          ]
        }
      ],
      "source": [
        "!mkdir -p ~/.kaggle\n",
        "!cp /content/kaggle.json ~/.kaggle/\n",
        "!chmod 600 ~/.kaggle/kaggle.json\n",
        "!kaggle config set -n path -v /content"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4feg3Y3_2IJC",
        "outputId": "9740987b-57ec-4f18-dd8b-bfc2f27792ed"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Downloading cifar-10.zip to /content/competitions/cifar-10\n",
            " 96% 689M/715M [00:07<00:00, 43.7MB/s]\n",
            "100% 715M/715M [00:07<00:00, 106MB/s] \n"
          ]
        }
      ],
      "source": [
        "!kaggle competitions download -c cifar-10"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QDeB7tM12b9K",
        "outputId": "f38f28ea-e01a-411d-8a90-6954b1983335"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Archive:  /content/competitions/cifar-10/cifar-10.zip\n",
            "  inflating: sampleSubmission.csv    \n",
            "  inflating: test.7z                 \n",
            "  inflating: train.7z                \n",
            "  inflating: trainLabels.csv         \n"
          ]
        }
      ],
      "source": [
        "!unzip /content/competitions/cifar-10/cifar-10.zip"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NK7iEl7I2bRK",
        "outputId": "4b0c4bdc-9800-4fc3-dc90-c016af35b476"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Collecting py7zr\n",
            "  Downloading py7zr-1.0.0-py3-none-any.whl.metadata (17 kB)\n",
            "Collecting texttable (from py7zr)\n",
            "  Downloading texttable-1.7.0-py2.py3-none-any.whl.metadata (9.8 kB)\n",
            "Requirement already satisfied: pycryptodomex>=3.20.0 in /usr/local/lib/python3.11/dist-packages (from py7zr) (3.23.0)\n",
            "Collecting brotli>=1.1.0 (from py7zr)\n",
            "  Downloading Brotli-1.1.0-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (5.5 kB)\n",
            "Requirement already satisfied: psutil in /usr/local/lib/python3.11/dist-packages (from py7zr) (5.9.5)\n",
            "Collecting pyzstd>=0.16.1 (from py7zr)\n",
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            "Collecting pyppmd<1.3.0,>=1.1.0 (from py7zr)\n",
            "  Downloading pyppmd-1.2.0-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (5.4 kB)\n",
            "Collecting pybcj<1.1.0,>=1.0.0 (from py7zr)\n",
            "  Downloading pybcj-1.0.6-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (3.7 kB)\n",
            "Collecting multivolumefile>=0.2.3 (from py7zr)\n",
            "  Downloading multivolumefile-0.2.3-py3-none-any.whl.metadata (6.3 kB)\n",
            "Collecting inflate64<1.1.0,>=1.0.0 (from py7zr)\n",
            "  Downloading inflate64-1.0.3-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (4.4 kB)\n",
            "Requirement already satisfied: typing-extensions>=4.13.2 in /usr/local/lib/python3.11/dist-packages (from pyzstd>=0.16.1->py7zr) (4.14.0)\n",
            "Downloading py7zr-1.0.0-py3-none-any.whl (69 kB)\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m69.7/69.7 kB\u001b[0m \u001b[31m3.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hDownloading Brotli-1.1.0-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (2.9 MB)\n",
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            "\u001b[?25hDownloading multivolumefile-0.2.3-py3-none-any.whl (17 kB)\n",
            "Downloading pybcj-1.0.6-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (50 kB)\n",
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            "\u001b[?25hDownloading pyppmd-1.2.0-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (141 kB)\n",
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            "\u001b[?25hDownloading pyzstd-0.17.0-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (412 kB)\n",
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            "\u001b[?25hDownloading texttable-1.7.0-py2.py3-none-any.whl (10 kB)\n",
            "Installing collected packages: texttable, brotli, pyzstd, pyppmd, pybcj, multivolumefile, inflate64, py7zr\n",
            "Successfully installed brotli-1.1.0 inflate64-1.0.3 multivolumefile-0.2.3 py7zr-1.0.0 pybcj-1.0.6 pyppmd-1.2.0 pyzstd-0.17.0 texttable-1.7.0\n"
          ]
        }
      ],
      "source": [
        "%pip install py7zr\n",
        "import py7zr\n",
        "a =py7zr.SevenZipFile(r'./train.7z','r')\n",
        "a.extractall(path=r'./competitions/cifar-10/')\n",
        "a.close()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rI5JDfji59q-",
        "outputId": "a14ac2ef-473f-4103-b176-758ae28cc8aa"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "50000\n"
          ]
        }
      ],
      "source": [
        "!ls competitions/cifar-10/train|wc -l"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vk4EQTiM4pNt"
      },
      "source": [
        "# 加载数据并处理为tensor"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "ExecuteTime": {
          "end_time": "2025-06-26T01:43:32.407799Z",
          "start_time": "2025-06-26T01:43:32.363026Z"
        },
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nvguuJLl4pNt",
        "outputId": "cbaeafb3-2e3b-456c-90b1-fc1fb2c951a5"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "完整数据集大小: 50000\n",
            "训练集大小: 45000\n",
            "验证集大小: 5000\n"
          ]
        }
      ],
      "source": [
        "# 加载CIFAR-10数据集\n",
        "import os\n",
        "import pandas as pd\n",
        "from PIL import Image\n",
        "from torch.utils.data import Dataset\n",
        "\n",
        "# 定义CIFAR-10数据集类\n",
        "class CIFAR10Dataset(Dataset):\n",
        "    def __init__(self, img_dir, labels_file, transform=None):\n",
        "        self.img_dir = img_dir\n",
        "        self.transform = transform\n",
        "\n",
        "        # 读取标签文件，read_csv默认读取第一行作为列名\n",
        "        self.labels_df = pd.read_csv(labels_file)\n",
        "        self.img_names = self.labels_df.iloc[:, 0].values.astype(str)  # 第一列是图片名称，确保为字符串类型\n",
        "\n",
        "        # 类别名称字典，使用字典可以提高查找速度\n",
        "        self.class_names_dict = {'airplane': 0, 'automobile': 1, 'bird': 2, 'cat': 3,\n",
        "                                 'deer': 4, 'dog': 5, 'frog': 6, 'horse': 7, 'ship': 8, 'truck': 9}\n",
        "        # 将文本标签转换为数字ID\n",
        "        self.labels = [self.class_names_dict[label] for label in self.labels_df.iloc[:, 1].values]\n",
        "\n",
        "    def __len__(self):\n",
        "        return len(self.labels)\n",
        "\n",
        "    def __getitem__(self, idx):\n",
        "        img_path = os.path.join(self.img_dir, self.img_names[idx] + '.png') #图片路径\n",
        "        image = Image.open(img_path) #打开图片\n",
        "        label = self.labels[idx]\n",
        "\n",
        "        if self.transform:\n",
        "            image_tensor = self.transform(image)\n",
        "\n",
        "        return image_tensor, label\n",
        "\n",
        "# 定义数据预处理\n",
        "transform = transforms.Compose([\n",
        "    transforms.ToTensor(),\n",
        "    transforms.Normalize((0.4917, 0.4823, 0.4467), (0.2024, 0.1995, 0.2010))\n",
        "])\n",
        "\n",
        "# 加载CIFAR-10数据集\n",
        "img_dir = r\"competitions/cifar-10/train\"\n",
        "labels_file = r\"./trainLabels.csv\"\n",
        "# img_dir = r\"D:\\cifar-10\\train\\train\"\n",
        "# labels_file = r\"D:\\cifar-10\\trainLabels.csv\"\n",
        "full_dataset = CIFAR10Dataset(img_dir=img_dir, labels_file=labels_file, transform=transform)\n",
        "\n",
        "# 定义类别名称\n",
        "class_names = ['airplane', 'automobile', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship', 'truck']\n",
        "\n",
        "# 划分训练集和验证集\n",
        "train_size = 45000\n",
        "val_size = 5000\n",
        "generator = torch.Generator().manual_seed(42)\n",
        "train_dataset, val_dataset = torch.utils.data.random_split(\n",
        "    full_dataset,\n",
        "    [train_size, val_size],\n",
        "    generator=generator\n",
        ")\n",
        "\n",
        "# 查看数据集基本信息\n",
        "print(f\"完整数据集大小: {len(full_dataset)}\")\n",
        "print(f\"训练集大小: {len(train_dataset)}\")\n",
        "print(f\"验证集大小: {len(val_dataset)}\")\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "id": "1akKUts84pNu"
      },
      "outputs": [],
      "source": [
        "def cal_mean_std(ds):\n",
        "    mean = 0.\n",
        "    std = 0.\n",
        "    for img, _ in ds:\n",
        "        mean += img.mean(dim=(1, 2)) #dim=(1, 2)表示在通道维度上求平均\n",
        "        std += img.std(dim=(1, 2))  #dim=(1, 2)表示在通道维度上求标准差\n",
        "    mean /= len(ds)\n",
        "    std /= len(ds)\n",
        "    return mean, std\n",
        "# cal_mean_std(train_dataset)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HrTSD6iw4pNu"
      },
      "source": [
        "# 把数据集划分为训练集45000和验证集5000，并给DataLoader"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "ExecuteTime": {
          "end_time": "2025-06-26T01:43:33.144223Z",
          "start_time": "2025-06-26T01:43:33.135368Z"
        },
        "id": "qK_zQ__r4pNu"
      },
      "outputs": [],
      "source": [
        "\n",
        "# 创建数据加载器\n",
        "batch_size = 64\n",
        "train_loader = torch.utils.data.DataLoader(\n",
        "    train_dataset,\n",
        "    batch_size=batch_size,\n",
        "    shuffle=True #打乱数据集，每次迭代时，数据集的顺序都会被打乱\n",
        ")\n",
        "\n",
        "val_loader = torch.utils.data.DataLoader(\n",
        "    val_dataset,\n",
        "    batch_size=batch_size,\n",
        "    shuffle=False\n",
        ")\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KUyAkERd4pNu"
      },
      "source": [
        "# 搭建模型"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "j17TXWWx4pNu",
        "outputId": "ff31b741-4b3a-4e9b-9bf1-331a7c027e88"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "torch.Size([20, 100])\n"
          ]
        }
      ],
      "source": [
        "#理解每个接口的方法，单独写例子\n",
        "import torch.nn as nn\n",
        "m=nn.BatchNorm1d(100)\n",
        "x=torch.randn(20,100)\n",
        "print(m(x).shape)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cFvbdkKd4pNu"
      },
      "source": [
        "# 搭建模型"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "ExecuteTime": {
          "end_time": "2025-06-26T01:43:33.152657Z",
          "start_time": "2025-06-26T01:43:33.148120Z"
        },
        "id": "UOfee2qW4pNu"
      },
      "outputs": [],
      "source": [
        "# 定义残差块类\n",
        "class ResidualBlock(nn.Module):\n",
        "    def __init__(self, in_channels, out_channels, stride=1):\n",
        "        super().__init__()\n",
        "\n",
        "        # 第一个卷积层\n",
        "        self.conv1 = nn.Conv2d(in_channels, out_channels, kernel_size=3, stride=stride, padding=1, bias=False)\n",
        "        self.bn1 = nn.BatchNorm2d(out_channels)\n",
        "        self.relu = nn.ReLU(inplace=True)\n",
        "\n",
        "        # 第二个卷积层\n",
        "        self.conv2 = nn.Conv2d(out_channels, out_channels, kernel_size=3, stride=1, padding=1, bias=False)\n",
        "        self.bn2 = nn.BatchNorm2d(out_channels)\n",
        "\n",
        "        # 如果输入和输出通道数不同或步长不为1，则需要使用1x1卷积进行调整\n",
        "        self.shortcut = nn.Sequential()\n",
        "        if stride != 1 or in_channels != out_channels:\n",
        "            self.shortcut = nn.Sequential(\n",
        "                nn.Conv2d(in_channels, out_channels, kernel_size=1, stride=stride, bias=False),\n",
        "                nn.BatchNorm2d(out_channels)\n",
        "            )\n",
        "\n",
        "    def forward(self, x):\n",
        "        residual = x\n",
        "\n",
        "        out = self.conv1(x)\n",
        "        out = self.bn1(out)\n",
        "        out = self.relu(out)\n",
        "\n",
        "        out = self.conv2(out)\n",
        "        out = self.bn2(out)\n",
        "\n",
        "        out += self.shortcut(residual) # 残差连接\n",
        "        out = self.relu(out)\n",
        "\n",
        "        return out\n",
        "\n",
        "# 定义ResNet18模型\n",
        "class ResNet18(nn.Module):\n",
        "    def __init__(self, num_classes=10):\n",
        "        super().__init__()\n",
        "\n",
        "        # 初始卷积层\n",
        "        self.conv1 = nn.Conv2d(3, 64, kernel_size=3, stride=1, padding=1, bias=False)\n",
        "        self.bn1 = nn.BatchNorm2d(64)\n",
        "        self.relu = nn.ReLU(inplace=True)\n",
        "\n",
        "        # 残差层\n",
        "        self.layer1 = self._make_layer(64, 64, 2, stride=1)\n",
        "        self.layer2 = self._make_layer(64, 128, 2, stride=2)\n",
        "        self.layer3 = self._make_layer(128, 256, 2, stride=2)\n",
        "        self.layer4 = self._make_layer(256, 512, 2, stride=2)\n",
        "\n",
        "        # 全局平均池化和全连接层\n",
        "        self.avgpool = nn.AdaptiveAvgPool2d((1, 1))\n",
        "        self.fc = nn.Linear(512, num_classes)\n",
        "\n",
        "    def _make_layer(self, in_channels, out_channels, num_blocks, stride):\n",
        "        \"\"\"\n",
        "        创建一个残差层\n",
        "        :param in_channels: 输入通道数\n",
        "        :param out_channels: 输出通道数\n",
        "        :param num_blocks: 残差块数量\n",
        "        :param stride: 步长\n",
        "        :return: 残差层\n",
        "        \"\"\"\n",
        "        layers = []\n",
        "        # 第一个残差块可能需要调整通道数和特征图大小\n",
        "        layers.append(ResidualBlock(in_channels, out_channels, stride))\n",
        "\n",
        "        # 后续残差块的输入通道数已经是out_channels\n",
        "        for _ in range(1, num_blocks):\n",
        "            layers.append(ResidualBlock(out_channels, out_channels))\n",
        "\n",
        "        return nn.Sequential(*layers) #*解包\n",
        "\n",
        "    def forward(self, x):\n",
        "        # print(f\"输入形状: {x.shape}\")\n",
        "\n",
        "        x = self.conv1(x)\n",
        "        # print(f\"conv1后形状: {x.shape}\")\n",
        "        x = self.bn1(x)\n",
        "        x = self.relu(x)\n",
        "\n",
        "        x = self.layer1(x)\n",
        "        # print(f\"layer1后形状: {x.shape}\")\n",
        "        x = self.layer2(x) #输出shape是[1, 128, 16, 16]\n",
        "        # print(f\"layer2后形状: {x.shape}\")\n",
        "        x = self.layer3(x) #输出shape是[1, 256, 8, 8]\n",
        "        # print(f\"layer3后形状: {x.shape}\")\n",
        "        x = self.layer4(x) #输出shape是[1, 512, 4, 4]\n",
        "        # print(f\"layer4后形状: {x.shape}\")\n",
        "\n",
        "        x = self.avgpool(x)\n",
        "        # print(f\"avgpool后形状: {x.shape}\")\n",
        "        x = torch.flatten(x, 1)\n",
        "        # print(f\"flatten后形状: {x.shape}\")\n",
        "        x = self.fc(x)\n",
        "        # print(f\"fc后形状: {x.shape}\")\n",
        "\n",
        "        return x\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "ExecuteTime": {
          "end_time": "2025-06-26T01:43:33.185031Z",
          "start_time": "2025-06-26T01:43:33.152657Z"
        },
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5Ll8FXqD4pNv",
        "outputId": "4517347c-daba-45f3-f910-8a71a65faeef"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "批次图像形状: torch.Size([64, 3, 32, 32])\n",
            "批次标签形状: torch.Size([64])\n",
            "----------------------------------------------------------------------------------------------------\n",
            "torch.Size([64, 10])\n"
          ]
        }
      ],
      "source": [
        "# 实例化模型\n",
        "model = ResNet18()\n",
        "\n",
        "# 从train_loader获取第一个批次的数据\n",
        "dataiter = iter(train_loader)\n",
        "images, labels = next(dataiter)\n",
        "\n",
        "# 查看批次数据的形状\n",
        "print(\"批次图像形状:\", images.shape)\n",
        "print(\"批次标签形状:\", labels.shape)\n",
        "\n",
        "\n",
        "print('-'*100)\n",
        "# 进行前向传播\n",
        "with torch.no_grad():  # 不需要计算梯度\n",
        "    outputs = model(images)\n",
        "\n",
        "\n",
        "print(outputs.shape)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "ExecuteTime": {
          "end_time": "2025-06-26T01:43:33.203053Z",
          "start_time": "2025-06-26T01:43:33.199532Z"
        },
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "V8zEsAla4pNv",
        "outputId": "f431137d-eeea-4e5c-b054-99ea82cdce5a"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "需要求梯度的参数总量: 11173962\n",
            "模型总参数量: 11173962\n",
            "\n",
            "各层参数量明细:\n",
            "conv1.weight: 1728 参数\n",
            "bn1.weight: 64 参数\n",
            "bn1.bias: 64 参数\n",
            "layer1.0.conv1.weight: 36864 参数\n",
            "layer1.0.bn1.weight: 64 参数\n",
            "layer1.0.bn1.bias: 64 参数\n",
            "layer1.0.conv2.weight: 36864 参数\n",
            "layer1.0.bn2.weight: 64 参数\n",
            "layer1.0.bn2.bias: 64 参数\n",
            "layer1.1.conv1.weight: 36864 参数\n",
            "layer1.1.bn1.weight: 64 参数\n",
            "layer1.1.bn1.bias: 64 参数\n",
            "layer1.1.conv2.weight: 36864 参数\n",
            "layer1.1.bn2.weight: 64 参数\n",
            "layer1.1.bn2.bias: 64 参数\n",
            "layer2.0.conv1.weight: 73728 参数\n",
            "layer2.0.bn1.weight: 128 参数\n",
            "layer2.0.bn1.bias: 128 参数\n",
            "layer2.0.conv2.weight: 147456 参数\n",
            "layer2.0.bn2.weight: 128 参数\n",
            "layer2.0.bn2.bias: 128 参数\n",
            "layer2.0.shortcut.0.weight: 8192 参数\n",
            "layer2.0.shortcut.1.weight: 128 参数\n",
            "layer2.0.shortcut.1.bias: 128 参数\n",
            "layer2.1.conv1.weight: 147456 参数\n",
            "layer2.1.bn1.weight: 128 参数\n",
            "layer2.1.bn1.bias: 128 参数\n",
            "layer2.1.conv2.weight: 147456 参数\n",
            "layer2.1.bn2.weight: 128 参数\n",
            "layer2.1.bn2.bias: 128 参数\n",
            "layer3.0.conv1.weight: 294912 参数\n",
            "layer3.0.bn1.weight: 256 参数\n",
            "layer3.0.bn1.bias: 256 参数\n",
            "layer3.0.conv2.weight: 589824 参数\n",
            "layer3.0.bn2.weight: 256 参数\n",
            "layer3.0.bn2.bias: 256 参数\n",
            "layer3.0.shortcut.0.weight: 32768 参数\n",
            "layer3.0.shortcut.1.weight: 256 参数\n",
            "layer3.0.shortcut.1.bias: 256 参数\n",
            "layer3.1.conv1.weight: 589824 参数\n",
            "layer3.1.bn1.weight: 256 参数\n",
            "layer3.1.bn1.bias: 256 参数\n",
            "layer3.1.conv2.weight: 589824 参数\n",
            "layer3.1.bn2.weight: 256 参数\n",
            "layer3.1.bn2.bias: 256 参数\n",
            "layer4.0.conv1.weight: 1179648 参数\n",
            "layer4.0.bn1.weight: 512 参数\n",
            "layer4.0.bn1.bias: 512 参数\n",
            "layer4.0.conv2.weight: 2359296 参数\n",
            "layer4.0.bn2.weight: 512 参数\n",
            "layer4.0.bn2.bias: 512 参数\n",
            "layer4.0.shortcut.0.weight: 131072 参数\n",
            "layer4.0.shortcut.1.weight: 512 参数\n",
            "layer4.0.shortcut.1.bias: 512 参数\n",
            "layer4.1.conv1.weight: 2359296 参数\n",
            "layer4.1.bn1.weight: 512 参数\n",
            "layer4.1.bn1.bias: 512 参数\n",
            "layer4.1.conv2.weight: 2359296 参数\n",
            "layer4.1.bn2.weight: 512 参数\n",
            "layer4.1.bn2.bias: 512 参数\n",
            "fc.weight: 5120 参数\n",
            "fc.bias: 10 参数\n"
          ]
        }
      ],
      "source": [
        "# 计算模型的总参数量\n",
        "# 统计需要求梯度的参数总量\n",
        "total_params = sum(p.numel() for p in model.parameters() if p.requires_grad)\n",
        "print(f\"需要求梯度的参数总量: {total_params}\")\n",
        "\n",
        "# 统计所有参数总量\n",
        "all_params = sum(p.numel() for p in model.parameters())\n",
        "print(f\"模型总参数量: {all_params}\")\n",
        "\n",
        "# 查看每层参数量明细\n",
        "print(\"\\n各层参数量明细:\")\n",
        "for name, param in model.named_parameters():\n",
        "    print(f\"{name}: {param.numel()} 参数\")\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0XQuUiCe4pNv",
        "outputId": "acf090ff-9b53-47ac-d18e-d6c42077ca37"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "294912"
            ]
          },
          "execution_count": 16,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "128*3*3*256"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1B2dFDE14pNv"
      },
      "source": [
        "# 各层参数量明细:\n",
        "conv1.weight: 288 参数 3*3*1*32\n",
        "conv1.bias: 32 参数\n",
        "conv2.weight: 9216 参数 3*3*32*32\n",
        "conv2.bias: 32 参数  \n",
        "conv3.weight: 18432 参数 3*3*32*64\n",
        "conv3.bias: 64 参数\n",
        "conv4.weight: 36864 参数  3*3*64*64\n",
        "conv4.bias: 64 参数\n",
        "conv5.weight: 73728 参数\n",
        "conv5.bias: 128 参数\n",
        "conv6.weight: 147456 参数\n",
        "conv6.bias: 128 参数\n",
        "fc1.weight: 294912 参数 128*3*3*256\n",
        "fc1.bias: 256 参数\n",
        "fc2.weight: 2560 参数\n",
        "fc2.bias: 10 参数"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "ExecuteTime": {
          "end_time": "2025-06-26T01:43:33.217395Z",
          "start_time": "2025-06-26T01:43:33.203561Z"
        },
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "al9xZTJQ4pNv",
        "outputId": "8393f9da-03d8-42a4-e08e-5cc00447bce1"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "OrderedDict([('conv1.weight',\n",
              "              tensor([[[[-0.0244,  0.1644,  0.1108],\n",
              "                        [ 0.1236,  0.1829, -0.1537],\n",
              "                        [-0.0621, -0.0367,  0.0360]],\n",
              "              \n",
              "                       [[-0.1881,  0.1642,  0.0962],\n",
              "                        [-0.1403, -0.0186, -0.0130],\n",
              "                        [-0.0535, -0.0286,  0.0613]],\n",
              "              \n",
              "                       [[ 0.1424, -0.0128,  0.0299],\n",
              "                        [ 0.1897,  0.1212, -0.1148],\n",
              "                        [-0.0188,  0.1342,  0.1673]]],\n",
              "              \n",
              "              \n",
              "                      [[[ 0.1341, -0.0442, -0.1905],\n",
              "                        [ 0.1240,  0.0795, -0.1216],\n",
              "                        [-0.1094,  0.0044,  0.0728]],\n",
              "              \n",
              "                       [[ 0.0410, -0.1140, -0.1193],\n",
              "                        [-0.1329, -0.1172,  0.1457],\n",
              "                        [-0.1068,  0.1721,  0.0062]],\n",
              "              \n",
              "                       [[ 0.0106,  0.0349,  0.0236],\n",
              "                        [-0.1171, -0.0296, -0.1330],\n",
              "                        [ 0.1318,  0.0598,  0.1751]]],\n",
              "              \n",
              "              \n",
              "                      [[[-0.0067, -0.0291,  0.0813],\n",
              "                        [ 0.0496,  0.1118,  0.0357],\n",
              "                        [-0.1871,  0.1362, -0.1625]],\n",
              "              \n",
              "                       [[-0.0638,  0.0993, -0.1776],\n",
              "                        [ 0.1097,  0.0172,  0.1028],\n",
              "                        [ 0.1878,  0.0892, -0.0014]],\n",
              "              \n",
              "                       [[-0.0305,  0.1399,  0.1886],\n",
              "                        [ 0.1400, -0.1715, -0.0664],\n",
              "                        [ 0.0456, -0.0711,  0.0566]]],\n",
              "              \n",
              "              \n",
              "                      ...,\n",
              "              \n",
              "              \n",
              "                      [[[ 0.0877, -0.0859,  0.0467],\n",
              "                        [ 0.1801, -0.1379,  0.1820],\n",
              "                        [-0.1350,  0.0781, -0.0267]],\n",
              "              \n",
              "                       [[ 0.1441, -0.1699, -0.0450],\n",
              "                        [-0.1550, -0.1021,  0.0762],\n",
              "                        [ 0.1909, -0.0247, -0.0545]],\n",
              "              \n",
              "                       [[ 0.1476, -0.0430, -0.0788],\n",
              "                        [-0.0155, -0.0380,  0.0225],\n",
              "                        [-0.1424,  0.0503,  0.0771]]],\n",
              "              \n",
              "              \n",
              "                      [[[ 0.0820,  0.0733, -0.1018],\n",
              "                        [ 0.1679,  0.0715,  0.0155],\n",
              "                        [ 0.0077,  0.1242, -0.0956]],\n",
              "              \n",
              "                       [[ 0.1254,  0.0564, -0.1040],\n",
              "                        [-0.0472, -0.0620, -0.0909],\n",
              "                        [-0.0984,  0.0156,  0.1269]],\n",
              "              \n",
              "                       [[-0.1398,  0.1716, -0.0105],\n",
              "                        [ 0.0438,  0.1839,  0.0946],\n",
              "                        [-0.1045,  0.1694,  0.0396]]],\n",
              "              \n",
              "              \n",
              "                      [[[ 0.0257,  0.1035, -0.1366],\n",
              "                        [-0.1615, -0.1102,  0.1856],\n",
              "                        [-0.1889,  0.1558, -0.1473]],\n",
              "              \n",
              "                       [[-0.0675, -0.1787,  0.0674],\n",
              "                        [ 0.1103,  0.1851,  0.1749],\n",
              "                        [ 0.1310,  0.0272,  0.1384]],\n",
              "              \n",
              "                       [[ 0.0677,  0.1136,  0.0818],\n",
              "                        [-0.0512,  0.0032, -0.1869],\n",
              "                        [ 0.1262, -0.1210,  0.1049]]]])),\n",
              "             ('bn1.weight',\n",
              "              tensor([1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,\n",
              "                      1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,\n",
              "                      1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,\n",
              "                      1., 1., 1., 1., 1., 1., 1., 1., 1., 1.])),\n",
              "             ('bn1.bias',\n",
              "              tensor([0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
              "                      0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
              "                      0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.])),\n",
              "             ('bn1.running_mean',\n",
              "              tensor([-2.6507e-03, -3.2841e-04, -1.6785e-03, -1.4169e-03, -8.9499e-04,\n",
              "                       7.2828e-04, -1.2334e-03,  8.2670e-04,  2.4804e-04,  2.7432e-03,\n",
              "                       1.0221e-03, -4.2776e-03, -4.5611e-03, -8.5929e-05,  6.4087e-04,\n",
              "                      -3.2203e-03,  6.4945e-04, -1.2104e-03, -1.5525e-03, -2.4905e-03,\n",
              "                      -3.7851e-04,  1.8091e-03, -3.9511e-03, -1.5429e-03, -3.1648e-03,\n",
              "                       2.6094e-03, -9.9000e-04, -5.6322e-04, -8.2186e-04,  5.9002e-04,\n",
              "                      -3.5477e-03,  3.1758e-03,  2.6115e-03, -1.9094e-03, -1.2685e-03,\n",
              "                      -2.5022e-03,  1.8943e-03,  1.9660e-03, -2.6873e-06,  3.0041e-04,\n",
              "                       1.0426e-03, -4.1751e-05,  2.5821e-04, -1.6351e-03,  1.4787e-03,\n",
              "                      -1.8700e-03,  8.5662e-04, -2.4369e-04,  9.4493e-04, -1.0611e-03,\n",
              "                      -1.8512e-03, -7.2878e-04, -3.5677e-04, -6.9461e-04,  1.1265e-03,\n",
              "                      -7.1223e-04, -1.2256e-03, -2.5395e-06, -7.3290e-04,  6.5900e-04,\n",
              "                      -3.1843e-04, -2.7695e-04, -2.1909e-03, -1.1660e-03])),\n",
              "             ('bn1.running_var',\n",
              "              tensor([1.0115, 0.9121, 0.9589, 0.9449, 0.9150, 0.9298, 0.9740, 0.9361, 0.9190,\n",
              "                      0.9887, 0.9722, 1.1252, 1.2497, 0.9142, 0.9115, 1.0457, 0.9351, 0.9442,\n",
              "                      0.9677, 0.9921, 0.9386, 0.9805, 1.1648, 0.9533, 1.0379, 1.0352, 0.9228,\n",
              "                      0.9131, 0.9329, 0.9104, 1.0167, 1.0812, 1.0012, 1.0457, 0.9325, 0.9818,\n",
              "                      0.9411, 0.9484, 0.9105, 0.9170, 0.9591, 0.9131, 0.9091, 0.9951, 0.9556,\n",
              "                      0.9360, 0.9434, 0.9070, 0.9204, 0.9295, 1.0197, 0.9234, 0.9179, 0.9071,\n",
              "                      0.9719, 0.9146, 0.9281, 0.9118, 0.9096, 0.9269, 0.9084, 0.9046, 0.9820,\n",
              "                      0.9357])),\n",
              "             ('bn1.num_batches_tracked', tensor(1)),\n",
              "             ('layer1.0.conv1.weight',\n",
              "              tensor([[[[-0.0301, -0.0178,  0.0025],\n",
              "                        [-0.0031,  0.0014,  0.0087],\n",
              "                        [ 0.0335, -0.0057,  0.0004]],\n",
              "              \n",
              "                       [[-0.0351,  0.0079,  0.0178],\n",
              "                        [ 0.0009, -0.0016, -0.0286],\n",
              "                        [ 0.0333,  0.0140,  0.0126]],\n",
              "              \n",
              "                       [[-0.0348,  0.0201, -0.0059],\n",
              "                        [ 0.0002,  0.0085,  0.0378],\n",
              "                        [ 0.0359, -0.0410,  0.0245]],\n",
              "              \n",
              "                       ...,\n",
              "              \n",
              "                       [[ 0.0328,  0.0069,  0.0017],\n",
              "                        [-0.0136, -0.0300,  0.0166],\n",
              "                        [ 0.0358, -0.0232,  0.0312]],\n",
              "              \n",
              "                       [[-0.0064, -0.0261,  0.0254],\n",
              "                        [ 0.0303, -0.0260, -0.0100],\n",
              "                        [-0.0087,  0.0263,  0.0299]],\n",
              "              \n",
              "                       [[ 0.0200,  0.0344,  0.0359],\n",
              "                        [-0.0312,  0.0087, -0.0064],\n",
              "                        [-0.0327, -0.0239, -0.0404]]],\n",
              "              \n",
              "              \n",
              "                      [[[ 0.0363, -0.0280, -0.0159],\n",
              "                        [-0.0194,  0.0330,  0.0377],\n",
              "                        [ 0.0251, -0.0356,  0.0253]],\n",
              "              \n",
              "                       [[ 0.0014,  0.0342,  0.0227],\n",
              "                        [ 0.0336,  0.0227, -0.0189],\n",
              "                        [ 0.0103, -0.0090,  0.0195]],\n",
              "              \n",
              "                       [[ 0.0324, -0.0392, -0.0078],\n",
              "                        [ 0.0012, -0.0267,  0.0356],\n",
              "                        [-0.0245, -0.0161,  0.0045]],\n",
              "              \n",
              "                       ...,\n",
              "              \n",
              "                       [[-0.0381,  0.0156,  0.0127],\n",
              "                        [ 0.0107, -0.0128,  0.0317],\n",
              "                        [ 0.0001, -0.0349, -0.0072]],\n",
              "              \n",
              "                       [[ 0.0210, -0.0276,  0.0085],\n",
              "                        [-0.0196,  0.0416,  0.0294],\n",
              "                        [ 0.0299,  0.0297, -0.0135]],\n",
              "              \n",
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              "              \n",
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              "                        [ 0.0278, -0.0073,  0.0186]],\n",
              "              \n",
              "                       [[ 0.0179, -0.0361, -0.0133],\n",
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              "                        [-0.0287,  0.0301,  0.0305]],\n",
              "              \n",
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              "                        [-0.0248, -0.0245, -0.0226]],\n",
              "              \n",
              "                       ...,\n",
              "              \n",
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              "                        [ 0.0104,  0.0118,  0.0079],\n",
              "                        [ 0.0404,  0.0138,  0.0396]],\n",
              "              \n",
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              "                        [-0.0216, -0.0306, -0.0293],\n",
              "                        [-0.0300, -0.0116,  0.0131]],\n",
              "              \n",
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              "                        [-0.0387, -0.0068,  0.0189],\n",
              "                        [ 0.0352, -0.0138,  0.0186]]],\n",
              "              \n",
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              "              \n",
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              "                        [-0.0098,  0.0312, -0.0212],\n",
              "                        [-0.0238, -0.0154, -0.0277]],\n",
              "              \n",
              "                       [[-0.0181, -0.0058,  0.0149],\n",
              "                        [-0.0224, -0.0340,  0.0387],\n",
              "                        [ 0.0089, -0.0396, -0.0030]],\n",
              "              \n",
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              "                        [ 0.0129,  0.0278, -0.0362],\n",
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              "              \n",
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              "              \n",
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              "                        [-0.0182,  0.0079, -0.0409],\n",
              "                        [ 0.0110, -0.0163,  0.0342]],\n",
              "              \n",
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              "                        [-0.0290, -0.0223, -0.0143]],\n",
              "              \n",
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              "              \n",
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              "              \n",
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              "                        [-0.0234,  0.0085, -0.0377]],\n",
              "              \n",
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              "              \n",
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              "                        [-0.0242,  0.0201,  0.0266],\n",
              "                        [-0.0174,  0.0263, -0.0410]],\n",
              "              \n",
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              "              \n",
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              "              \n",
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              "              \n",
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              "              \n",
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              "              \n",
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              "              \n",
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              "                        [ 0.0376,  0.0072, -0.0309],\n",
              "                        [-0.0056, -0.0243,  0.0072]]]])),\n",
              "             ('layer1.0.bn1.weight',\n",
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              "             ('layer1.0.bn1.running_mean',\n",
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              "                      -2.7716e-03, -7.8787e-03, -3.7706e-02,  2.7720e-02])),\n",
              "             ('layer1.0.bn1.running_var',\n",
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              "                      0.9100])),\n",
              "             ('layer1.0.bn1.num_batches_tracked', tensor(1)),\n",
              "             ('layer1.0.conv2.weight',\n",
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              "                        [ 1.2389e-02, -2.7545e-02,  2.6516e-03]],\n",
              "              \n",
              "                       [[ 1.8761e-02,  3.0880e-03, -2.5900e-02],\n",
              "                        [-2.7489e-02, -1.9359e-03,  9.4046e-05],\n",
              "                        [ 3.0257e-02,  2.2050e-02,  2.4079e-02]],\n",
              "              \n",
              "                       [[ 5.5143e-03, -3.1398e-02,  4.0107e-02],\n",
              "                        [ 3.9301e-02, -6.6195e-03, -8.5705e-03],\n",
              "                        [-1.9745e-02,  4.2163e-03, -5.5009e-04]],\n",
              "              \n",
              "                       ...,\n",
              "              \n",
              "                       [[ 3.5360e-02, -4.0504e-02, -1.3931e-02],\n",
              "                        [-1.0383e-02,  1.3151e-02,  2.4804e-02],\n",
              "                        [ 3.8748e-02,  1.5992e-02, -2.9179e-02]],\n",
              "              \n",
              "                       [[-8.2481e-03, -1.1580e-02,  8.3222e-03],\n",
              "                        [-1.2177e-02, -3.0157e-02,  2.6688e-02],\n",
              "                        [ 2.1691e-02, -3.0330e-02,  3.1046e-02]],\n",
              "              \n",
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              "                        [-2.9532e-02, -1.6890e-02, -1.1534e-02],\n",
              "                        [ 3.7320e-02,  2.0084e-02,  4.3596e-03]]],\n",
              "              \n",
              "              \n",
              "                      [[[ 2.1162e-02, -8.6033e-03, -1.3082e-02],\n",
              "                        [ 2.9808e-02, -1.8712e-02, -3.4128e-02],\n",
              "                        [-3.7461e-02,  7.7252e-03, -3.2018e-02]],\n",
              "              \n",
              "                       [[-8.3249e-03,  4.1583e-02, -1.1811e-02],\n",
              "                        [-1.7388e-02, -3.2196e-02, -1.3777e-02],\n",
              "                        [-1.4433e-02, -1.7509e-02, -1.9884e-02]],\n",
              "              \n",
              "                       [[ 1.0487e-02,  1.0373e-02, -4.0936e-02],\n",
              "                        [ 1.5487e-02,  1.0567e-02,  3.2756e-02],\n",
              "                        [ 2.2226e-02,  2.8474e-02, -3.5330e-02]],\n",
              "              \n",
              "                       ...,\n",
              "              \n",
              "                       [[-1.8606e-02,  4.0655e-02,  3.4153e-02],\n",
              "                        [ 1.0023e-02,  4.9636e-04, -2.3793e-02],\n",
              "                        [-3.4260e-04,  3.9881e-02,  1.3870e-02]],\n",
              "              \n",
              "                       [[ 1.9611e-02,  3.3146e-02, -1.7480e-03],\n",
              "                        [ 2.8299e-02,  2.9109e-02,  1.2456e-02],\n",
              "                        [-8.0707e-03, -2.3269e-02,  3.1211e-03]],\n",
              "              \n",
              "                       [[ 1.8920e-02, -1.5956e-02,  1.7604e-03],\n",
              "                        [ 5.0440e-03,  5.3070e-03,  3.4915e-02],\n",
              "                        [-2.4522e-02, -1.6640e-02,  2.2405e-02]]],\n",
              "              \n",
              "              \n",
              "                      [[[-3.1802e-02,  3.4582e-02, -2.8872e-03],\n",
              "                        [-4.1562e-02,  3.1803e-03,  1.7117e-02],\n",
              "                        [ 2.1498e-02, -3.8059e-02,  3.4356e-02]],\n",
              "              \n",
              "                       [[-1.3089e-02,  2.2313e-03, -4.1553e-02],\n",
              "                        [-3.8112e-02,  2.9117e-02, -3.1091e-02],\n",
              "                        [-3.9550e-02,  3.5118e-02,  1.7048e-03]],\n",
              "              \n",
              "                       [[ 3.9153e-02, -8.4178e-03,  1.0687e-03],\n",
              "                        [ 2.6570e-02, -6.4802e-03, -3.2992e-02],\n",
              "                        [-2.2245e-02, -2.9451e-02,  3.6249e-02]],\n",
              "              \n",
              "                       ...,\n",
              "              \n",
              "                       [[-3.9970e-02, -9.1908e-03,  1.6140e-02],\n",
              "                        [ 3.2992e-02,  3.3920e-02, -1.1953e-02],\n",
              "                        [ 2.3541e-02, -2.3469e-02,  1.6094e-02]],\n",
              "              \n",
              "                       [[ 2.3017e-02,  1.7967e-02, -1.3092e-04],\n",
              "                        [ 1.0940e-02, -2.6033e-02, -1.1587e-02],\n",
              "                        [ 3.9717e-02, -2.1161e-02,  2.3244e-02]],\n",
              "              \n",
              "                       [[-3.7601e-03, -4.0073e-03,  2.6719e-02],\n",
              "                        [ 5.7782e-03, -3.7651e-02,  2.0336e-02],\n",
              "                        [-6.0415e-03, -3.3776e-03,  5.4402e-03]]],\n",
              "              \n",
              "              \n",
              "                      ...,\n",
              "              \n",
              "              \n",
              "                      [[[ 4.2015e-03,  3.7162e-02,  3.3238e-02],\n",
              "                        [ 3.1570e-02,  4.5045e-03, -2.3391e-02],\n",
              "                        [-2.3141e-02, -1.6046e-02, -1.2434e-02]],\n",
              "              \n",
              "                       [[-3.0281e-02,  1.5806e-02,  2.0400e-02],\n",
              "                        [-2.9233e-02,  3.6909e-02,  3.6171e-02],\n",
              "                        [-9.3453e-03,  4.2700e-03,  2.8245e-03]],\n",
              "              \n",
              "                       [[-1.9112e-02, -3.5444e-02,  2.8290e-03],\n",
              "                        [-1.3489e-02,  3.3698e-02, -2.6267e-02],\n",
              "                        [ 2.9708e-02,  3.5644e-02,  5.2442e-03]],\n",
              "              \n",
              "                       ...,\n",
              "              \n",
              "                       [[ 2.0024e-02, -2.3853e-02,  2.7666e-02],\n",
              "                        [-2.9203e-02,  3.7458e-02,  2.6010e-02],\n",
              "                        [ 3.5435e-02,  3.9609e-02, -5.8406e-03]],\n",
              "              \n",
              "                       [[-1.9628e-02,  2.9193e-02, -1.2216e-02],\n",
              "                        [ 6.9817e-03,  3.0085e-02,  3.9724e-02],\n",
              "                        [ 3.1463e-03,  1.8522e-02, -2.3016e-02]],\n",
              "              \n",
              "                       [[-3.6453e-02,  1.2147e-02, -3.3977e-02],\n",
              "                        [ 2.2311e-02,  3.6939e-02,  1.9963e-02],\n",
              "                        [-1.4557e-02,  1.9088e-02, -2.4586e-02]]],\n",
              "              \n",
              "              \n",
              "                      [[[-3.3474e-02,  2.5165e-02, -9.5017e-03],\n",
              "                        [-3.3607e-02, -3.4374e-02, -4.0849e-02],\n",
              "                        [ 2.7497e-03,  2.1573e-03, -3.8738e-02]],\n",
              "              \n",
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              "                        [-5.7886e-03, -3.3388e-02,  2.7769e-02],\n",
              "                        [-1.7198e-02, -2.5640e-02,  3.9928e-02]],\n",
              "              \n",
              "                       [[ 2.9821e-02, -3.6675e-02,  1.9453e-02],\n",
              "                        [-1.3856e-02, -2.2731e-02, -3.6034e-02],\n",
              "                        [-3.9093e-02,  7.8983e-03,  3.8707e-02]],\n",
              "              \n",
              "                       ...,\n",
              "              \n",
              "                       [[-2.7433e-02,  1.7357e-02,  2.8545e-02],\n",
              "                        [-4.0939e-02, -3.9943e-02,  8.5907e-03],\n",
              "                        [-2.3572e-02,  3.2052e-03, -9.4173e-04]],\n",
              "              \n",
              "                       [[ 2.7796e-02, -1.6870e-02,  3.9828e-02],\n",
              "                        [ 2.1964e-02,  1.1280e-02,  3.6136e-02],\n",
              "                        [-1.2136e-02, -1.7186e-02,  3.5636e-02]],\n",
              "              \n",
              "                       [[ 2.8177e-02, -9.6833e-03, -2.4334e-03],\n",
              "                        [ 1.4563e-02, -3.8369e-03, -3.1298e-02],\n",
              "                        [-3.4153e-02, -1.1774e-02, -1.3741e-02]]],\n",
              "              \n",
              "              \n",
              "                      [[[ 6.3102e-03, -3.9036e-02,  5.7706e-03],\n",
              "                        [-1.9696e-02,  1.8384e-02, -7.9258e-03],\n",
              "                        [ 2.3816e-02, -3.9152e-02, -3.4567e-02]],\n",
              "              \n",
              "                       [[-1.4988e-02, -2.8948e-02, -1.7356e-02],\n",
              "                        [-2.5583e-02, -3.4467e-02,  3.6063e-03],\n",
              "                        [-4.4766e-03, -2.6467e-02,  3.6169e-02]],\n",
              "              \n",
              "                       [[ 3.2046e-02, -1.4914e-02,  3.1116e-02],\n",
              "                        [-3.7002e-02,  2.0563e-02,  3.4325e-02],\n",
              "                        [-3.2324e-02,  8.9349e-03, -2.8202e-02]],\n",
              "              \n",
              "                       ...,\n",
              "              \n",
              "                       [[ 3.1635e-02,  3.0500e-02,  2.9002e-03],\n",
              "                        [ 2.9448e-02, -2.2783e-03, -2.0017e-02],\n",
              "                        [ 3.5926e-02, -1.3574e-03,  3.8738e-02]],\n",
              "              \n",
              "                       [[ 2.5521e-02, -1.2608e-02, -1.1325e-02],\n",
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              "              \n",
              "                       [[-2.5408e-02, -3.1867e-02,  1.1997e-02],\n",
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              "                        [ 3.1268e-02, -2.8948e-02, -2.3889e-02]]]])),\n",
              "             ('layer1.0.bn2.weight',\n",
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              "                      1., 1., 1., 1., 1., 1., 1., 1., 1., 1.])),\n",
              "             ('layer1.0.bn2.bias',\n",
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              "             ('layer1.0.bn2.running_mean',\n",
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              "                       0.0516,  0.0522,  0.0331, -0.0214, -0.0221,  0.0017, -0.0056,  0.0284,\n",
              "                      -0.0157, -0.0229, -0.0135,  0.0116, -0.0098, -0.0019, -0.0274, -0.0037,\n",
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              "                      -0.0254, -0.0300,  0.0015, -0.0093,  0.0194, -0.0358,  0.0271, -0.0399,\n",
              "                      -0.0179,  0.0325,  0.0407, -0.0058, -0.0145,  0.0292,  0.0059, -0.0286,\n",
              "                      -0.0019,  0.0062,  0.0216, -0.0324, -0.0081,  0.0035,  0.0191, -0.0375,\n",
              "                      -0.0205, -0.0222,  0.0107, -0.0449, -0.0311,  0.0267, -0.0441,  0.0178])),\n",
              "             ('layer1.0.bn2.running_var',\n",
              "              tensor([0.9081, 0.9098, 0.9087, 0.9092, 0.9148, 0.9061, 0.9114, 0.9206, 0.9087,\n",
              "                      0.9153, 0.9125, 0.9066, 0.9111, 0.9239, 0.9085, 0.9082, 0.9089, 0.9098,\n",
              "                      0.9092, 0.9065, 0.9093, 0.9112, 0.9117, 0.9081, 0.9116, 0.9245, 0.9092,\n",
              "                      0.9117, 0.9178, 0.9114, 0.9132, 0.9079, 0.9178, 0.9143, 0.9077, 0.9136,\n",
              "                      0.9106, 0.9096, 0.9088, 0.9176, 0.9126, 0.9082, 0.9263, 0.9123, 0.9153,\n",
              "                      0.9120, 0.9114, 0.9099, 0.9144, 0.9067, 0.9130, 0.9090, 0.9117, 0.9081,\n",
              "                      0.9132, 0.9127, 0.9108, 0.9052, 0.9074, 0.9091, 0.9211, 0.9149, 0.9134,\n",
              "                      0.9103])),\n",
              "             ('layer1.0.bn2.num_batches_tracked', tensor(1)),\n",
              "             ('layer1.1.conv1.weight',\n",
              "              tensor([[[[-1.2065e-02,  4.5412e-04,  1.5291e-02],\n",
              "                        [ 1.8012e-02,  1.2761e-02,  3.7452e-02],\n",
              "                        [ 3.9007e-02,  3.1076e-02, -1.0151e-02]],\n",
              "              \n",
              "                       [[-1.7659e-02,  8.3866e-03, -1.2046e-02],\n",
              "                        [-2.9948e-03,  8.3962e-03, -8.0256e-03],\n",
              "                        [ 2.6487e-02, -2.4259e-02, -1.9383e-03]],\n",
              "              \n",
              "                       [[-7.7096e-03,  1.2729e-02, -5.3123e-03],\n",
              "                        [ 1.8247e-02,  1.4845e-02, -1.6119e-02],\n",
              "                        [ 3.7492e-02,  1.6059e-02, -7.1966e-03]],\n",
              "              \n",
              "                       ...,\n",
              "              \n",
              "                       [[-5.3544e-03, -3.9478e-02,  6.1395e-04],\n",
              "                        [ 1.6209e-02,  6.7707e-03,  3.4164e-02],\n",
              "                        [ 8.1554e-03, -3.0768e-02,  2.6445e-02]],\n",
              "              \n",
              "                       [[ 1.7421e-02,  2.6327e-02, -1.7133e-02],\n",
              "                        [ 3.3678e-02, -3.8134e-02, -2.4522e-02],\n",
              "                        [ 2.6348e-02,  2.3839e-02,  3.4023e-02]],\n",
              "              \n",
              "                       [[-3.2991e-02,  2.1573e-02,  1.6882e-03],\n",
              "                        [ 3.9172e-03, -1.0617e-02,  3.5141e-03],\n",
              "                        [ 2.1588e-02,  6.5666e-03,  1.4027e-02]]],\n",
              "              \n",
              "              \n",
              "                      [[[-2.7837e-02, -4.0999e-02,  1.2384e-02],\n",
              "                        [-4.3654e-03,  7.0083e-03, -2.4138e-02],\n",
              "                        [ 3.8488e-02,  3.1164e-02,  1.3656e-02]],\n",
              "              \n",
              "                       [[-2.5453e-02,  1.9587e-02,  6.3732e-03],\n",
              "                        [-1.5387e-02, -2.9671e-02, -3.7271e-02],\n",
              "                        [ 7.0783e-03, -1.9332e-02,  1.1656e-03]],\n",
              "              \n",
              "                       [[ 3.6923e-02, -3.7962e-02,  1.3875e-02],\n",
              "                        [ 3.8708e-02,  3.8990e-02, -2.7688e-02],\n",
              "                        [ 2.5007e-02,  3.5454e-02,  2.7745e-02]],\n",
              "              \n",
              "                       ...,\n",
              "              \n",
              "                       [[ 3.3734e-02, -4.1550e-02, -2.5705e-02],\n",
              "                        [ 2.2828e-02, -6.9766e-03,  1.9022e-02],\n",
              "                        [-3.6155e-02, -1.5824e-02,  1.9077e-02]],\n",
              "              \n",
              "                       [[-2.5071e-02, -3.0205e-02,  1.0924e-02],\n",
              "                        [-6.1719e-03, -3.5848e-02,  6.1960e-03],\n",
              "                        [ 6.5023e-03,  2.0558e-02, -3.0574e-02]],\n",
              "              \n",
              "                       [[-2.6708e-02,  2.2204e-03,  3.2390e-04],\n",
              "                        [ 1.6195e-02, -1.3669e-02, -2.3446e-02],\n",
              "                        [-2.4950e-02, -7.0370e-03,  3.9031e-02]]],\n",
              "              \n",
              "              \n",
              "                      [[[ 2.8512e-02,  3.3286e-03,  3.8185e-03],\n",
              "                        [ 3.0455e-02,  2.6573e-02, -8.2803e-03],\n",
              "                        [ 4.3496e-05,  4.0224e-02,  3.8749e-02]],\n",
              "              \n",
              "                       [[ 2.2218e-02,  1.1662e-02, -1.2433e-02],\n",
              "                        [-3.3249e-02, -3.9027e-02, -6.1858e-03],\n",
              "                        [-3.3749e-02, -3.4158e-02, -1.7789e-02]],\n",
              "              \n",
              "                       [[-2.8165e-02,  2.8389e-02, -2.3664e-02],\n",
              "                        [ 4.0406e-03, -1.8026e-02, -1.6202e-02],\n",
              "                        [-5.2098e-03,  3.2834e-02,  1.5941e-02]],\n",
              "              \n",
              "                       ...,\n",
              "              \n",
              "                       [[-1.2131e-02, -5.0678e-03,  1.4461e-02],\n",
              "                        [-2.2995e-03,  2.8089e-02,  3.5345e-02],\n",
              "                        [-3.7396e-02,  2.5708e-02, -2.8987e-02]],\n",
              "              \n",
              "                       [[-1.3038e-02,  4.1249e-02, -2.3135e-02],\n",
              "                        [ 2.7268e-02,  1.9087e-02,  1.9991e-02],\n",
              "                        [-3.4614e-02, -8.1771e-03, -4.1508e-02]],\n",
              "              \n",
              "                       [[ 4.8764e-03, -3.8161e-02,  2.9546e-02],\n",
              "                        [-2.1891e-02, -2.4102e-02, -3.6228e-02],\n",
              "                        [-1.3825e-02, -2.2803e-02, -1.8421e-02]]],\n",
              "              \n",
              "              \n",
              "                      ...,\n",
              "              \n",
              "              \n",
              "                      [[[ 6.9978e-03, -3.1441e-03, -1.6755e-02],\n",
              "                        [ 3.6481e-02,  3.4309e-02,  2.0554e-02],\n",
              "                        [-1.9899e-02,  3.0378e-02, -1.4265e-02]],\n",
              "              \n",
              "                       [[ 7.0827e-04,  3.5877e-02, -2.9737e-02],\n",
              "                        [-9.4860e-03, -1.3721e-02, -2.6696e-02],\n",
              "                        [ 2.5254e-02,  1.0049e-02, -1.5222e-02]],\n",
              "              \n",
              "                       [[ 2.2977e-02,  4.2744e-03,  1.0982e-02],\n",
              "                        [-2.0459e-02,  6.6191e-03,  8.2595e-03],\n",
              "                        [-4.1608e-02, -1.1212e-02,  1.1070e-02]],\n",
              "              \n",
              "                       ...,\n",
              "              \n",
              "                       [[ 1.9030e-02,  1.6822e-02,  3.6629e-02],\n",
              "                        [-3.6541e-02,  2.1660e-02, -2.8846e-02],\n",
              "                        [-8.2710e-03,  3.9217e-02, -1.2306e-02]],\n",
              "              \n",
              "                       [[ 3.7117e-02,  3.2656e-02, -1.6480e-02],\n",
              "                        [ 3.8526e-02,  3.6225e-02, -4.0114e-02],\n",
              "                        [-1.4911e-02,  2.9452e-02,  7.3116e-03]],\n",
              "              \n",
              "                       [[-3.8893e-02,  2.3591e-02,  1.6913e-02],\n",
              "                        [ 3.5225e-02,  3.7427e-02,  3.2849e-02],\n",
              "                        [-3.9528e-02,  9.5094e-03,  2.5851e-02]]],\n",
              "              \n",
              "              \n",
              "                      [[[-2.8025e-02,  2.2124e-02, -2.0422e-02],\n",
              "                        [ 1.5083e-02,  2.7875e-02,  6.4665e-03],\n",
              "                        [-1.2872e-02,  4.0802e-02, -3.6243e-02]],\n",
              "              \n",
              "                       [[ 1.8002e-02,  3.4648e-02, -3.8667e-02],\n",
              "                        [-3.5410e-02,  1.6941e-02, -2.7503e-02],\n",
              "                        [-3.3327e-02, -2.1676e-02, -2.8050e-03]],\n",
              "              \n",
              "                       [[-2.2124e-03, -8.4613e-03,  1.8554e-02],\n",
              "                        [-1.6591e-02,  2.2857e-02,  2.0284e-02],\n",
              "                        [-2.5322e-02, -3.0172e-02, -2.1826e-02]],\n",
              "              \n",
              "                       ...,\n",
              "              \n",
              "                       [[ 1.9843e-02, -1.9596e-02, -5.3033e-03],\n",
              "                        [ 2.1045e-02,  2.1070e-02,  4.0556e-02],\n",
              "                        [-1.9898e-02, -6.3971e-03,  2.1842e-02]],\n",
              "              \n",
              "                       [[-3.9439e-03, -2.0002e-02, -7.2242e-03],\n",
              "                        [-2.7003e-02,  2.9364e-04, -3.9642e-02],\n",
              "                        [-1.8090e-03, -2.2296e-02,  1.1649e-02]],\n",
              "              \n",
              "                       [[ 1.1601e-02, -2.3857e-02, -3.5146e-02],\n",
              "                        [-3.6849e-02,  2.3841e-02,  3.0544e-02],\n",
              "                        [ 1.2854e-02, -2.6790e-02,  1.3834e-02]]],\n",
              "              \n",
              "              \n",
              "                      [[[ 1.5049e-02,  6.9873e-03, -1.5578e-02],\n",
              "                        [ 6.9478e-03,  4.4685e-03,  2.6189e-02],\n",
              "                        [ 3.7038e-02, -1.3112e-02,  1.5501e-02]],\n",
              "              \n",
              "                       [[ 2.8450e-02,  4.2518e-03, -3.9898e-02],\n",
              "                        [ 4.5529e-03,  1.4485e-02,  4.7880e-03],\n",
              "                        [-1.4082e-02,  3.5041e-02, -9.4779e-03]],\n",
              "              \n",
              "                       [[-4.3006e-03, -2.4185e-02,  3.3495e-02],\n",
              "                        [-7.0814e-03, -3.2619e-02, -2.5420e-02],\n",
              "                        [-2.2645e-02, -1.3428e-02, -2.5866e-02]],\n",
              "              \n",
              "                       ...,\n",
              "              \n",
              "                       [[-4.6457e-03, -2.5650e-02,  3.0533e-02],\n",
              "                        [-1.1394e-02,  4.0978e-02,  4.1024e-03],\n",
              "                        [ 8.1202e-03,  4.0924e-02,  1.3992e-02]],\n",
              "              \n",
              "                       [[-3.2451e-02, -7.0239e-03,  9.6849e-03],\n",
              "                        [-1.5412e-02, -1.5717e-02, -6.8828e-03],\n",
              "                        [ 1.2133e-02,  3.4613e-03,  9.8897e-03]],\n",
              "              \n",
              "                       [[ 4.5277e-03,  3.9627e-02, -2.1289e-03],\n",
              "                        [ 5.1622e-03, -4.0037e-02,  7.3348e-03],\n",
              "                        [-2.3295e-02,  9.3966e-03,  2.6719e-02]]]])),\n",
              "             ('layer1.1.bn1.weight',\n",
              "              tensor([1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,\n",
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              "                      1., 1., 1., 1., 1., 1., 1., 1., 1., 1.])),\n",
              "             ('layer1.1.bn1.bias',\n",
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              "             ('layer1.1.bn1.running_mean',\n",
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              "                       0.0316,  0.0607,  0.0424,  0.0070, -0.0261,  0.0273, -0.0345,  0.0485,\n",
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              "                       0.0414, -0.0939,  0.0161,  0.0084, -0.0352,  0.0393,  0.0058,  0.0307])),\n",
              "             ('layer1.1.bn1.running_var',\n",
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              "                      0.9161, 0.9372, 0.9142, 0.9397, 0.9189, 0.9208, 0.9304, 0.9248, 0.9274,\n",
              "                      0.9213])),\n",
              "             ('layer1.1.bn1.num_batches_tracked', tensor(1)),\n",
              "             ('layer1.1.conv2.weight',\n",
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              "              \n",
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              "              \n",
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              "              \n",
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              "              \n",
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              "              \n",
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              "              \n",
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              "              \n",
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              "              \n",
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              "              \n",
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              "                        [-0.0358,  0.0334,  0.0387],\n",
              "                        [ 0.0342,  0.0125, -0.0232]]],\n",
              "              \n",
              "              \n",
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              "              \n",
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              "                        [-0.0194, -0.0062, -0.0089]],\n",
              "              \n",
              "                       [[-0.0126, -0.0278, -0.0010],\n",
              "                        [-0.0094, -0.0345, -0.0351],\n",
              "                        [ 0.0288, -0.0119,  0.0306]],\n",
              "              \n",
              "                       ...,\n",
              "              \n",
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              "                        [-0.0411,  0.0144, -0.0065],\n",
              "                        [-0.0181,  0.0229, -0.0299]],\n",
              "              \n",
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              "                        [ 0.0261,  0.0090, -0.0033],\n",
              "                        [-0.0082,  0.0002, -0.0115]],\n",
              "              \n",
              "                       [[ 0.0023, -0.0345, -0.0063],\n",
              "                        [ 0.0094,  0.0039, -0.0284],\n",
              "                        [-0.0362, -0.0188,  0.0023]]],\n",
              "              \n",
              "              \n",
              "                      ...,\n",
              "              \n",
              "              \n",
              "                      [[[-0.0256,  0.0157, -0.0152],\n",
              "                        [ 0.0034, -0.0222,  0.0151],\n",
              "                        [-0.0126,  0.0004,  0.0354]],\n",
              "              \n",
              "                       [[-0.0080,  0.0318,  0.0112],\n",
              "                        [-0.0411,  0.0208,  0.0010],\n",
              "                        [-0.0211,  0.0208,  0.0380]],\n",
              "              \n",
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              "                        [-0.0331,  0.0285, -0.0153],\n",
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              "              \n",
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              "                        [ 0.0143, -0.0112, -0.0314],\n",
              "                        [-0.0318,  0.0320,  0.0095]],\n",
              "              \n",
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              "              \n",
              "                       [[-0.0413,  0.0004,  0.0304],\n",
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              "              \n",
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              "              \n",
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              "              \n",
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              "              \n",
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              "              \n",
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              "              \n",
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              "              \n",
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              "              \n",
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              "              \n",
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              "              \n",
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              "              \n",
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              "                        [ 0.0198, -0.0292, -0.0014],\n",
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              "             ('layer1.1.bn2.weight',\n",
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              "             ('layer1.1.bn2.bias',\n",
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              "             ('layer1.1.bn2.running_mean',\n",
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              "                       0.0223,  0.0235,  0.0222,  0.0082,  0.0254,  0.0013,  0.0121, -0.0002])),\n",
              "             ('layer1.1.bn2.running_var',\n",
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              "                      0.9123, 0.9080, 0.9093, 0.9079, 0.9124, 0.9091, 0.9101, 0.9083, 0.9220,\n",
              "                      0.9095])),\n",
              "             ('layer1.1.bn2.num_batches_tracked', tensor(1)),\n",
              "             ('layer2.0.conv1.weight',\n",
              "              tensor([[[[ 2.8515e-02, -2.5218e-02, -3.0824e-02],\n",
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              "                        [ 4.2414e-03,  4.1172e-02, -2.9669e-03]],\n",
              "              \n",
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              "              \n",
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              "                        [ 2.2945e-02,  2.4186e-02, -4.1348e-02]],\n",
              "              \n",
              "                       ...,\n",
              "              \n",
              "                       [[ 1.3235e-02,  2.8340e-02,  7.9022e-03],\n",
              "                        [-3.4027e-03,  2.3440e-02, -3.6475e-02],\n",
              "                        [ 9.1420e-03, -2.5840e-02, -7.6638e-03]],\n",
              "              \n",
              "                       [[ 3.0771e-02, -2.4923e-02,  2.4823e-02],\n",
              "                        [ 1.4955e-02, -7.0685e-03, -3.7078e-02],\n",
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              "              \n",
              "                       [[-3.6072e-02, -3.1484e-02,  2.2580e-02],\n",
              "                        [-1.5008e-03, -3.4505e-02, -1.6777e-02],\n",
              "                        [ 4.4581e-03,  1.2589e-02,  3.1908e-02]]],\n",
              "              \n",
              "              \n",
              "                      [[[ 4.0487e-03,  1.5872e-03, -2.9159e-02],\n",
              "                        [-3.9430e-02, -3.9716e-02,  3.2075e-03],\n",
              "                        [-1.2716e-02, -3.9089e-02,  2.3608e-02]],\n",
              "              \n",
              "                       [[ 1.5260e-02,  2.3281e-02, -5.6505e-04],\n",
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              "                        [-2.2231e-02,  1.3941e-02,  3.1559e-02]],\n",
              "              \n",
              "                       [[ 2.4596e-02, -2.3891e-02, -4.1467e-02],\n",
              "                        [-1.3829e-02, -2.9673e-04, -7.5450e-03],\n",
              "                        [ 2.5313e-02,  1.5665e-02,  1.1593e-03]],\n",
              "              \n",
              "                       ...,\n",
              "              \n",
              "                       [[ 7.5110e-03, -5.1114e-03, -9.4252e-03],\n",
              "                        [ 2.4968e-02,  4.0697e-02, -1.2123e-02],\n",
              "                        [-1.3257e-04,  2.7854e-02, -1.5758e-02]],\n",
              "              \n",
              "                       [[ 1.4718e-02, -1.8605e-02, -3.7451e-02],\n",
              "                        [ 3.3661e-02,  1.3571e-02, -3.7007e-02],\n",
              "                        [-8.1947e-03,  8.2380e-03,  4.0318e-02]],\n",
              "              \n",
              "                       [[ 2.9837e-02,  4.0319e-02,  1.9944e-02],\n",
              "                        [ 1.2330e-02,  1.1001e-02, -1.9026e-02],\n",
              "                        [ 2.3330e-02, -2.4192e-02, -6.4220e-04]]],\n",
              "              \n",
              "              \n",
              "                      [[[-3.4968e-02,  5.9620e-03, -1.9694e-02],\n",
              "                        [-4.1286e-02,  2.9037e-02, -3.2652e-02],\n",
              "                        [ 1.0930e-02,  1.0392e-02, -1.6307e-02]],\n",
              "              \n",
              "                       [[-2.8028e-02, -3.2666e-02,  1.3557e-02],\n",
              "                        [ 3.5191e-02, -1.4578e-02, -3.9803e-02],\n",
              "                        [ 1.2786e-02, -3.5168e-02,  1.1905e-02]],\n",
              "              \n",
              "                       [[-8.2269e-03, -3.7431e-02, -1.0441e-02],\n",
              "                        [-3.2905e-03, -3.1150e-02, -1.3710e-02],\n",
              "                        [ 2.3104e-02, -2.1953e-02, -2.6386e-02]],\n",
              "              \n",
              "                       ...,\n",
              "              \n",
              "                       [[-3.7914e-02, -6.1895e-03, -1.2203e-02],\n",
              "                        [ 4.0633e-02,  2.2847e-02, -1.1587e-02],\n",
              "                        [ 3.6403e-02, -1.5056e-03, -2.7897e-02]],\n",
              "              \n",
              "                       [[-4.9511e-03,  1.1099e-02,  3.2396e-02],\n",
              "                        [ 2.6547e-02,  2.4009e-02, -1.4024e-02],\n",
              "                        [ 1.9375e-03, -3.3659e-03,  4.7570e-03]],\n",
              "              \n",
              "                       [[-2.4187e-02, -1.6497e-02,  2.7788e-02],\n",
              "                        [-2.5891e-02, -3.5896e-02,  2.6572e-03],\n",
              "                        [-4.2357e-03, -3.7211e-02,  2.0218e-02]]],\n",
              "              \n",
              "              \n",
              "                      ...,\n",
              "              \n",
              "              \n",
              "                      [[[-3.2933e-02,  3.9145e-02, -3.1031e-02],\n",
              "                        [-3.2152e-02,  4.4186e-03, -9.3403e-03],\n",
              "                        [-2.8633e-02,  4.1488e-02,  1.0784e-02]],\n",
              "              \n",
              "                       [[-9.4076e-03,  7.3654e-03, -1.4875e-02],\n",
              "                        [ 2.2086e-03, -3.9555e-02,  1.4396e-02],\n",
              "                        [-3.1925e-02,  6.4603e-03,  3.9232e-02]],\n",
              "              \n",
              "                       [[ 1.9043e-02,  1.4123e-02,  2.6553e-02],\n",
              "                        [ 2.3699e-02, -2.8297e-03,  3.0658e-02],\n",
              "                        [ 1.2340e-02,  3.9311e-02,  1.2116e-02]],\n",
              "              \n",
              "                       ...,\n",
              "              \n",
              "                       [[-3.0614e-02, -3.4242e-02, -2.8449e-02],\n",
              "                        [ 3.2661e-02,  2.6928e-02, -1.5445e-02],\n",
              "                        [-2.3106e-02, -2.6106e-02,  3.0545e-02]],\n",
              "              \n",
              "                       [[ 1.1705e-02,  3.9253e-02,  8.8279e-03],\n",
              "                        [-7.5761e-03, -2.6175e-02,  1.7251e-02],\n",
              "                        [ 1.3709e-02,  1.1291e-02,  1.0906e-02]],\n",
              "              \n",
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              "                        [ 2.3604e-02,  1.2783e-02,  2.7089e-02],\n",
              "                        [ 2.3587e-02,  8.5975e-03,  3.1793e-02]]],\n",
              "              \n",
              "              \n",
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              "              \n",
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              "              \n",
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              "                        [ 2.6253e-02, -1.5922e-02, -1.5050e-02]],\n",
              "              \n",
              "                       ...,\n",
              "              \n",
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              "                        [-1.4171e-02, -9.8259e-03, -2.7223e-02],\n",
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              "              \n",
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              "              \n",
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              "                        [-4.6882e-03, -1.4166e-04,  6.1077e-03],\n",
              "                        [-3.0754e-02, -6.4245e-03, -2.5366e-02]]],\n",
              "              \n",
              "              \n",
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              "              \n",
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              "              \n",
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              "                        [-2.4503e-02, -3.6260e-02, -2.5393e-02]],\n",
              "              \n",
              "                       ...,\n",
              "              \n",
              "                       [[ 2.7138e-03, -2.0223e-02, -9.1408e-03],\n",
              "                        [ 3.9017e-02,  1.9293e-03, -2.9323e-03],\n",
              "                        [-1.8325e-03, -2.2771e-03, -1.1457e-02]],\n",
              "              \n",
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              "              \n",
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              "                        [ 6.5719e-03, -1.5870e-02,  1.0194e-02],\n",
              "                        [-4.1171e-02,  2.7947e-03,  1.9130e-02]]]])),\n",
              "             ('layer2.0.bn1.weight',\n",
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              "             ('layer2.0.bn1.bias',\n",
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              "             ('layer2.0.bn1.running_mean',\n",
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              "                      -0.0033,  0.0098, -0.0286,  0.0231, -0.0368, -0.0151, -0.0798, -0.0198])),\n",
              "             ('layer2.0.bn1.running_var',\n",
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              "                      0.9408, 0.9273])),\n",
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              "                      -2.0519e-02, -6.0559e-04, -4.1846e-02])),\n",
              "             ('layer2.0.shortcut.1.running_var',\n",
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              "             ('layer2.1.conv1.weight',\n",
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              "              \n",
              "                       [[ 0.0273, -0.0112, -0.0170],\n",
              "                        [-0.0141,  0.0281, -0.0169],\n",
              "                        [-0.0265, -0.0047, -0.0160]],\n",
              "              \n",
              "                       ...,\n",
              "              \n",
              "                       [[ 0.0164,  0.0069, -0.0073],\n",
              "                        [ 0.0200, -0.0179, -0.0082],\n",
              "                        [ 0.0225, -0.0293,  0.0079]],\n",
              "              \n",
              "                       [[-0.0109, -0.0160, -0.0181],\n",
              "                        [ 0.0152, -0.0048, -0.0158],\n",
              "                        [-0.0234,  0.0016,  0.0180]],\n",
              "              \n",
              "                       [[ 0.0209,  0.0039, -0.0217],\n",
              "                        [-0.0260, -0.0144,  0.0123],\n",
              "                        [-0.0045,  0.0051, -0.0226]]],\n",
              "              \n",
              "              \n",
              "                      [[[-0.0101,  0.0185, -0.0220],\n",
              "                        [ 0.0291,  0.0063,  0.0237],\n",
              "                        [ 0.0006, -0.0166,  0.0040]],\n",
              "              \n",
              "                       [[ 0.0278,  0.0147,  0.0171],\n",
              "                        [-0.0263,  0.0276,  0.0021],\n",
              "                        [ 0.0286, -0.0267, -0.0230]],\n",
              "              \n",
              "                       [[-0.0142,  0.0177, -0.0144],\n",
              "                        [-0.0009, -0.0132, -0.0010],\n",
              "                        [ 0.0059, -0.0047, -0.0253]],\n",
              "              \n",
              "                       ...,\n",
              "              \n",
              "                       [[-0.0119,  0.0091,  0.0257],\n",
              "                        [-0.0091,  0.0126,  0.0077],\n",
              "                        [-0.0172,  0.0208, -0.0143]],\n",
              "              \n",
              "                       [[-0.0205,  0.0234, -0.0036],\n",
              "                        [-0.0052,  0.0210, -0.0204],\n",
              "                        [ 0.0152,  0.0132, -0.0100]],\n",
              "              \n",
              "                       [[ 0.0214,  0.0026, -0.0210],\n",
              "                        [ 0.0017,  0.0224, -0.0235],\n",
              "                        [-0.0270,  0.0029, -0.0170]]],\n",
              "              \n",
              "              \n",
              "                      [[[ 0.0131, -0.0105, -0.0037],\n",
              "                        [-0.0186, -0.0087,  0.0217],\n",
              "                        [-0.0184,  0.0167, -0.0153]],\n",
              "              \n",
              "                       [[-0.0236,  0.0095, -0.0051],\n",
              "                        [-0.0274,  0.0181,  0.0199],\n",
              "                        [ 0.0116,  0.0203,  0.0104]],\n",
              "              \n",
              "                       [[ 0.0051, -0.0059,  0.0064],\n",
              "                        [ 0.0137, -0.0101, -0.0188],\n",
              "                        [-0.0096, -0.0036,  0.0006]],\n",
              "              \n",
              "                       ...,\n",
              "              \n",
              "                       [[ 0.0200, -0.0124, -0.0042],\n",
              "                        [ 0.0080,  0.0228, -0.0182],\n",
              "                        [-0.0014, -0.0150,  0.0244]],\n",
              "              \n",
              "                       [[ 0.0161, -0.0244,  0.0103],\n",
              "                        [ 0.0293, -0.0135,  0.0255],\n",
              "                        [ 0.0058,  0.0178, -0.0209]],\n",
              "              \n",
              "                       [[-0.0278,  0.0026,  0.0157],\n",
              "                        [ 0.0241,  0.0201,  0.0151],\n",
              "                        [-0.0182,  0.0287, -0.0207]]],\n",
              "              \n",
              "              \n",
              "                      ...,\n",
              "              \n",
              "              \n",
              "                      [[[ 0.0196, -0.0276, -0.0191],\n",
              "                        [-0.0277, -0.0240, -0.0134],\n",
              "                        [-0.0271,  0.0086, -0.0103]],\n",
              "              \n",
              "                       [[ 0.0119, -0.0293,  0.0058],\n",
              "                        [ 0.0005,  0.0223, -0.0112],\n",
              "                        [ 0.0044,  0.0210,  0.0223]],\n",
              "              \n",
              "                       [[-0.0215,  0.0139,  0.0086],\n",
              "                        [ 0.0266, -0.0148, -0.0086],\n",
              "                        [ 0.0208,  0.0225, -0.0041]],\n",
              "              \n",
              "                       ...,\n",
              "              \n",
              "                       [[-0.0155,  0.0281, -0.0040],\n",
              "                        [ 0.0204,  0.0269, -0.0258],\n",
              "                        [-0.0036, -0.0012, -0.0291]],\n",
              "              \n",
              "                       [[-0.0196,  0.0019, -0.0129],\n",
              "                        [ 0.0067, -0.0217, -0.0013],\n",
              "                        [-0.0071,  0.0050,  0.0109]],\n",
              "              \n",
              "                       [[ 0.0090, -0.0130,  0.0043],\n",
              "                        [ 0.0155, -0.0087,  0.0006],\n",
              "                        [-0.0214, -0.0276, -0.0144]]],\n",
              "              \n",
              "              \n",
              "                      [[[-0.0222, -0.0139,  0.0029],\n",
              "                        [-0.0044,  0.0034, -0.0145],\n",
              "                        [-0.0251,  0.0086, -0.0207]],\n",
              "              \n",
              "                       [[-0.0205,  0.0128, -0.0256],\n",
              "                        [ 0.0049,  0.0259, -0.0173],\n",
              "                        [ 0.0111, -0.0168, -0.0164]],\n",
              "              \n",
              "                       [[-0.0188, -0.0172, -0.0234],\n",
              "                        [ 0.0055,  0.0212, -0.0159],\n",
              "                        [ 0.0112,  0.0020,  0.0109]],\n",
              "              \n",
              "                       ...,\n",
              "              \n",
              "                       [[-0.0052, -0.0140,  0.0210],\n",
              "                        [-0.0063, -0.0231, -0.0159],\n",
              "                        [-0.0163, -0.0136, -0.0238]],\n",
              "              \n",
              "                       [[ 0.0046,  0.0059, -0.0092],\n",
              "                        [-0.0192, -0.0036, -0.0212],\n",
              "                        [-0.0049, -0.0112,  0.0086]],\n",
              "              \n",
              "                       [[-0.0039, -0.0208, -0.0020],\n",
              "                        [-0.0128, -0.0039, -0.0140],\n",
              "                        [ 0.0125, -0.0205,  0.0134]]],\n",
              "              \n",
              "              \n",
              "                      [[[-0.0007,  0.0156, -0.0044],\n",
              "                        [-0.0191,  0.0034, -0.0148],\n",
              "                        [ 0.0066, -0.0263, -0.0065]],\n",
              "              \n",
              "                       [[-0.0293,  0.0231,  0.0045],\n",
              "                        [-0.0013, -0.0198,  0.0183],\n",
              "                        [-0.0059, -0.0151,  0.0044]],\n",
              "              \n",
              "                       [[-0.0131, -0.0237, -0.0206],\n",
              "                        [ 0.0190, -0.0117, -0.0291],\n",
              "                        [ 0.0076,  0.0212, -0.0005]],\n",
              "              \n",
              "                       ...,\n",
              "              \n",
              "                       [[ 0.0022,  0.0249, -0.0208],\n",
              "                        [ 0.0110, -0.0242,  0.0180],\n",
              "                        [ 0.0273,  0.0192, -0.0078]],\n",
              "              \n",
              "                       [[-0.0218, -0.0202, -0.0260],\n",
              "                        [ 0.0208,  0.0001,  0.0018],\n",
              "                        [ 0.0219,  0.0093, -0.0164]],\n",
              "              \n",
              "                       [[-0.0138,  0.0158,  0.0194],\n",
              "                        [-0.0065, -0.0198, -0.0078],\n",
              "                        [-0.0133,  0.0273, -0.0124]]]])),\n",
              "             ('layer2.1.bn2.weight',\n",
              "              tensor([1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,\n",
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              "                      1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,\n",
              "                      1., 1.])),\n",
              "             ('layer2.1.bn2.bias',\n",
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              "             ('layer2.1.bn2.running_mean',\n",
              "              tensor([ 0.0264,  0.0208,  0.0148,  0.0008,  0.0346, -0.0040,  0.0261, -0.0133,\n",
              "                       0.0245,  0.0152,  0.0424, -0.0113, -0.0071,  0.0157, -0.0207, -0.0084,\n",
              "                       0.0215,  0.0210, -0.0390,  0.0194,  0.0026,  0.0126, -0.0362,  0.0218,\n",
              "                       0.0063, -0.0014, -0.0211,  0.0384, -0.0012,  0.0111,  0.0211, -0.0219,\n",
              "                      -0.0101, -0.0070,  0.0008,  0.0268, -0.0158,  0.0053,  0.0075,  0.0099,\n",
              "                      -0.0061, -0.0151, -0.0088, -0.0291, -0.0168,  0.0126, -0.0176,  0.0082,\n",
              "                      -0.0176, -0.0342,  0.0065,  0.0039, -0.0403,  0.0286,  0.0190,  0.0137,\n",
              "                       0.0033, -0.0259,  0.0039,  0.0274, -0.0470, -0.0016, -0.0059, -0.0345,\n",
              "                       0.0110,  0.0042, -0.0214,  0.0581, -0.0235, -0.0412,  0.0240, -0.0278,\n",
              "                      -0.0069, -0.0161,  0.0068,  0.0118,  0.0247,  0.0023,  0.0018,  0.0151,\n",
              "                       0.0140,  0.0261, -0.0180,  0.0128,  0.0266,  0.0279, -0.0163,  0.0080,\n",
              "                       0.0194,  0.0298,  0.0123, -0.0122, -0.0021, -0.0185, -0.0406,  0.0527,\n",
              "                      -0.0045,  0.0060, -0.0231,  0.0483,  0.0350,  0.0127,  0.0100,  0.0326,\n",
              "                       0.0264,  0.0097,  0.0488, -0.0086, -0.0151,  0.0047,  0.0019, -0.0030,\n",
              "                       0.0216,  0.0187,  0.0347, -0.0301, -0.0125, -0.0021, -0.0345, -0.0021,\n",
              "                      -0.0090, -0.0393, -0.0166, -0.0213, -0.0092, -0.0078, -0.0553, -0.0251])),\n",
              "             ('layer2.1.bn2.running_var',\n",
              "              tensor([0.9094, 0.9105, 0.9088, 0.9116, 0.9151, 0.9095, 0.9115, 0.9132, 0.9135,\n",
              "                      0.9126, 0.9094, 0.9124, 0.9125, 0.9139, 0.9101, 0.9094, 0.9113, 0.9086,\n",
              "                      0.9122, 0.9159, 0.9125, 0.9100, 0.9112, 0.9090, 0.9147, 0.9099, 0.9133,\n",
              "                      0.9152, 0.9104, 0.9120, 0.9095, 0.9121, 0.9096, 0.9107, 0.9092, 0.9101,\n",
              "                      0.9147, 0.9099, 0.9101, 0.9110, 0.9112, 0.9119, 0.9101, 0.9104, 0.9095,\n",
              "                      0.9105, 0.9098, 0.9114, 0.9105, 0.9135, 0.9125, 0.9129, 0.9172, 0.9119,\n",
              "                      0.9098, 0.9107, 0.9101, 0.9121, 0.9143, 0.9100, 0.9110, 0.9096, 0.9115,\n",
              "                      0.9105, 0.9113, 0.9119, 0.9121, 0.9127, 0.9129, 0.9115, 0.9111, 0.9102,\n",
              "                      0.9119, 0.9129, 0.9113, 0.9092, 0.9113, 0.9097, 0.9098, 0.9124, 0.9110,\n",
              "                      0.9107, 0.9104, 0.9113, 0.9119, 0.9095, 0.9096, 0.9112, 0.9113, 0.9094,\n",
              "                      0.9109, 0.9133, 0.9095, 0.9113, 0.9139, 0.9143, 0.9101, 0.9098, 0.9099,\n",
              "                      0.9111, 0.9123, 0.9108, 0.9094, 0.9100, 0.9125, 0.9118, 0.9144, 0.9117,\n",
              "                      0.9116, 0.9083, 0.9094, 0.9129, 0.9103, 0.9097, 0.9149, 0.9102, 0.9108,\n",
              "                      0.9105, 0.9102, 0.9104, 0.9101, 0.9126, 0.9102, 0.9134, 0.9110, 0.9110,\n",
              "                      0.9154, 0.9126])),\n",
              "             ('layer2.1.bn2.num_batches_tracked', tensor(1)),\n",
              "             ('layer3.0.conv1.weight',\n",
              "              tensor([[[[ 1.8559e-02,  2.1090e-02, -2.5698e-02],\n",
              "                        [ 5.2338e-04, -2.0169e-02, -1.1330e-02],\n",
              "                        [ 8.0025e-03, -2.1403e-02, -2.5774e-02]],\n",
              "              \n",
              "                       [[ 1.1877e-02,  1.6252e-02,  1.1964e-02],\n",
              "                        [ 5.0437e-03, -2.2290e-02,  1.9586e-02],\n",
              "                        [ 1.4181e-02, -1.4808e-02,  1.9796e-02]],\n",
              "              \n",
              "                       [[ 5.7731e-04, -3.2913e-03, -1.4772e-02],\n",
              "                        [-6.5926e-03,  2.6017e-02, -1.5095e-02],\n",
              "                        [-3.3327e-03, -6.1046e-03, -1.9550e-02]],\n",
              "              \n",
              "                       ...,\n",
              "              \n",
              "                       [[ 2.1919e-02, -1.7671e-02,  1.5819e-02],\n",
              "                        [-2.5602e-02, -1.2733e-02, -1.4881e-02],\n",
              "                        [-5.6246e-03, -1.3432e-02,  8.1448e-03]],\n",
              "              \n",
              "                       [[ 1.1365e-02, -3.6990e-03, -1.8607e-02],\n",
              "                        [ 2.6748e-02, -2.9061e-03,  2.9300e-02],\n",
              "                        [-1.0284e-02,  2.1350e-03,  2.6218e-02]],\n",
              "              \n",
              "                       [[ 7.4147e-03, -1.6566e-02, -1.8019e-02],\n",
              "                        [ 2.3136e-02, -2.8159e-02,  5.8283e-03],\n",
              "                        [-1.1878e-02, -2.3223e-03,  2.2996e-02]]],\n",
              "              \n",
              "              \n",
              "                      [[[ 2.5073e-02,  2.5616e-02,  1.0222e-02],\n",
              "                        [ 2.8578e-02, -4.3114e-03, -1.3515e-02],\n",
              "                        [ 2.8523e-02,  2.7516e-02, -3.0908e-03]],\n",
              "              \n",
              "                       [[-2.3228e-02, -2.8478e-02, -4.1305e-03],\n",
              "                        [-2.5790e-02, -2.3390e-02,  1.7771e-02],\n",
              "                        [-1.7840e-02, -2.3951e-02, -8.3163e-03]],\n",
              "              \n",
              "                       [[-5.9659e-03,  1.7917e-02,  2.6830e-02],\n",
              "                        [-2.6212e-02,  1.5190e-02,  1.2036e-04],\n",
              "                        [ 5.0844e-03, -8.4364e-03, -1.5226e-02]],\n",
              "              \n",
              "                       ...,\n",
              "              \n",
              "                       [[-1.9235e-02, -1.4306e-02,  5.0916e-03],\n",
              "                        [-6.9951e-03,  1.3969e-02, -7.1003e-03],\n",
              "                        [-1.7668e-02,  2.5121e-02, -2.7195e-02]],\n",
              "              \n",
              "                       [[ 2.0061e-02, -7.8428e-03, -1.0031e-02],\n",
              "                        [ 5.1474e-03,  1.9492e-02, -2.7531e-02],\n",
              "                        [ 9.9270e-03, -1.7474e-02,  5.8547e-03]],\n",
              "              \n",
              "                       [[-6.3892e-03, -1.6755e-02, -7.1803e-03],\n",
              "                        [-1.1632e-02, -1.5286e-02, -6.7415e-03],\n",
              "                        [ 1.1482e-02, -1.9679e-02,  1.4664e-03]]],\n",
              "              \n",
              "              \n",
              "                      [[[-2.2957e-02, -2.5828e-02,  2.4718e-02],\n",
              "                        [ 2.6776e-02,  2.2933e-02, -2.6303e-02],\n",
              "                        [-1.1313e-02, -1.5947e-02,  6.3105e-03]],\n",
              "              \n",
              "                       [[-8.0291e-03, -1.7131e-02, -2.2647e-02],\n",
              "                        [-9.4741e-03,  2.7497e-02,  8.1877e-04],\n",
              "                        [-2.8422e-03, -3.9606e-03,  2.4925e-02]],\n",
              "              \n",
              "                       [[-1.2532e-03, -1.1096e-02, -3.6381e-03],\n",
              "                        [-1.4886e-02,  2.4732e-02, -1.5787e-02],\n",
              "                        [ 2.7299e-02,  2.2659e-02, -1.3788e-02]],\n",
              "              \n",
              "                       ...,\n",
              "              \n",
              "                       [[ 2.0395e-02, -2.6501e-02,  1.1815e-02],\n",
              "                        [-4.9064e-03, -2.2555e-02, -2.4492e-02],\n",
              "                        [ 1.2838e-02,  1.9142e-02, -2.2948e-02]],\n",
              "              \n",
              "                       [[-1.0241e-02, -1.8830e-02, -1.6777e-02],\n",
              "                        [-1.4034e-02, -1.4253e-03, -2.8827e-02],\n",
              "                        [ 8.4441e-03, -2.1777e-02, -2.4881e-02]],\n",
              "              \n",
              "                       [[ 1.7848e-02,  5.2211e-03, -1.6714e-02],\n",
              "                        [ 8.3700e-05, -7.3174e-03, -1.0400e-02],\n",
              "                        [-1.7490e-02,  2.6254e-02, -1.5291e-03]]],\n",
              "              \n",
              "              \n",
              "                      ...,\n",
              "              \n",
              "              \n",
              "                      [[[ 2.3007e-02, -6.0671e-03, -8.7580e-03],\n",
              "                        [ 1.2632e-02, -1.2322e-02,  8.8672e-03],\n",
              "                        [ 2.6573e-02, -2.2109e-02, -1.6668e-02]],\n",
              "              \n",
              "                       [[ 1.9520e-02, -2.2434e-03, -5.8403e-04],\n",
              "                        [ 2.3531e-02, -2.3141e-02,  1.8890e-02],\n",
              "                        [-3.5826e-03,  1.5937e-02,  1.5186e-02]],\n",
              "              \n",
              "                       [[-9.3811e-03, -1.6027e-02, -1.6949e-02],\n",
              "                        [-2.9202e-02,  2.0588e-03, -2.3172e-02],\n",
              "                        [ 1.8895e-02,  2.8542e-02, -2.3003e-02]],\n",
              "              \n",
              "                       ...,\n",
              "              \n",
              "                       [[ 1.2599e-02,  2.4118e-02,  6.8660e-03],\n",
              "                        [-1.2949e-02,  1.0676e-02,  1.6209e-02],\n",
              "                        [-1.6593e-02, -2.4626e-02, -6.7612e-03]],\n",
              "              \n",
              "                       [[ 1.6523e-03, -2.1273e-02, -3.2277e-03],\n",
              "                        [-2.4531e-02,  1.3288e-02, -2.2368e-02],\n",
              "                        [ 7.8384e-04,  1.4804e-02, -6.0774e-03]],\n",
              "              \n",
              "                       [[ 2.8878e-02, -2.1869e-02,  1.2978e-03],\n",
              "                        [-1.5134e-02,  2.7633e-02, -1.2800e-02],\n",
              "                        [-3.2410e-03,  2.8020e-02,  1.3794e-02]]],\n",
              "              \n",
              "              \n",
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              "              \n",
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              "              \n",
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              "              \n",
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              "              \n",
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              "              \n",
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              "              \n",
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              "              \n",
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              "              \n",
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              "              \n",
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              "              \n",
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              "              \n",
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              "              \n",
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              "              \n",
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              "              \n",
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              "              \n",
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              "              \n",
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              "              \n",
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              "              \n",
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              "                        [-2.1800e-03, -5.4048e-03,  1.6763e-02],\n",
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              "              \n",
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              "              \n",
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              "              \n",
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              "              \n",
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              "              \n",
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              "              \n",
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              "              \n",
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              "              \n",
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              "              \n",
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              "              \n",
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              "              \n",
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              "              \n",
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              "              \n",
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              "              \n",
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              "              \n",
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              "              \n",
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              "              \n",
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              "              \n",
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              "              \n",
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              "              \n",
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              "              \n",
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              "              \n",
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              "              \n",
              "              \n",
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              "              \n",
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              "              \n",
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              "              \n",
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              "                        [-1.9384e-02, -1.4288e-02,  1.7990e-02],\n",
              "                        [ 1.5240e-02, -1.9840e-02,  1.2881e-02]]]])),\n",
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              "             ('layer3.1.bn1.running_mean',\n",
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              "             ('layer3.1.conv2.weight',\n",
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              "              \n",
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              "              \n",
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              "              \n",
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              "              \n",
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              "              \n",
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              "              \n",
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              "              \n",
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              "              \n",
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              "              \n",
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              "              \n",
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              "              \n",
              "              \n",
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              "              \n",
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              "              \n",
              "                       [[ 6.7600e-04, -1.3027e-02,  1.7060e-02],\n",
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              "                        [ 3.3160e-03,  6.5077e-03, -1.8688e-02]],\n",
              "              \n",
              "                       ...,\n",
              "              \n",
              "                       [[ 1.4756e-02, -5.9940e-05, -7.9173e-03],\n",
              "                        [ 1.6039e-02,  4.5654e-03, -5.7183e-03],\n",
              "                        [-1.2760e-02,  5.6997e-03,  6.3300e-04]],\n",
              "              \n",
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              "                        [-4.4326e-03,  1.2737e-02,  1.6149e-02],\n",
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              "              \n",
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              "                        [ 1.5200e-02, -8.8505e-03,  1.4359e-02]]],\n",
              "              \n",
              "              \n",
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              "              \n",
              "              \n",
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              "                        [-1.3678e-02, -1.9517e-03,  1.5005e-02],\n",
              "                        [-7.7947e-03,  9.2830e-03,  1.2313e-02]],\n",
              "              \n",
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              "                        [-2.2992e-03,  1.7090e-02, -1.5342e-02],\n",
              "                        [ 2.0568e-02, -9.7142e-03, -3.1682e-03]],\n",
              "              \n",
              "                       [[ 1.7469e-02, -1.2193e-02,  1.1724e-02],\n",
              "                        [ 1.6613e-03,  6.1039e-03,  1.7161e-02],\n",
              "                        [-3.2293e-03,  2.7055e-03,  1.6787e-02]],\n",
              "              \n",
              "                       ...,\n",
              "              \n",
              "                       [[ 1.5881e-02,  2.0074e-03,  1.8621e-02],\n",
              "                        [ 3.4781e-03, -1.2391e-02, -1.5118e-02],\n",
              "                        [ 1.5359e-02, -5.2006e-03,  1.2648e-02]],\n",
              "              \n",
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              "                        [ 5.7731e-04,  5.8182e-03, -6.5999e-03]],\n",
              "              \n",
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              "                        [-2.4255e-03,  5.9512e-03,  1.0688e-02],\n",
              "                        [ 1.1828e-02, -2.2938e-03, -5.1617e-03]]],\n",
              "              \n",
              "              \n",
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              "              \n",
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              "                        [-3.9929e-03,  1.9948e-03,  1.1180e-02],\n",
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              "              \n",
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              "                        [-2.9939e-03, -1.5658e-02, -1.9446e-02],\n",
              "                        [ 1.5598e-02,  2.8834e-03,  1.1062e-02]],\n",
              "              \n",
              "                       ...,\n",
              "              \n",
              "                       [[ 1.6220e-02,  9.7235e-03, -2.0701e-02],\n",
              "                        [-9.6767e-03,  2.9094e-03, -1.2243e-02],\n",
              "                        [-1.2209e-02,  2.4476e-03,  1.1421e-02]],\n",
              "              \n",
              "                       [[-2.8078e-03, -1.8299e-02,  7.8618e-04],\n",
              "                        [-4.2374e-03, -9.0150e-03,  1.1314e-02],\n",
              "                        [ 1.9325e-02, -1.7057e-02,  1.7581e-02]],\n",
              "              \n",
              "                       [[ 1.0546e-02,  1.8827e-02,  7.0713e-04],\n",
              "                        [ 1.1337e-02, -1.7683e-02,  4.2790e-03],\n",
              "                        [ 1.7070e-02,  1.3051e-03,  1.1752e-02]]],\n",
              "              \n",
              "              \n",
              "                      [[[ 4.6852e-03, -2.0651e-02,  4.6398e-03],\n",
              "                        [-8.4578e-03,  9.7379e-04, -5.5760e-03],\n",
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              "              \n",
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              "              \n",
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              "              \n",
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              "              \n",
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              "              \n",
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              "              \n",
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              "              \n",
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              "              \n",
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              "              \n",
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              "              \n",
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              "              \n",
              "                       ...,\n",
              "              \n",
              "                       [[ 1.5928e-02, -5.2826e-03,  1.7153e-02],\n",
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              "              \n",
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              "              \n",
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              "              \n",
              "              \n",
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              "              \n",
              "              \n",
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              "              \n",
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              "              \n",
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              "                        [ 5.5858e-03,  2.8120e-04, -2.0894e-04]],\n",
              "              \n",
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              "              \n",
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              "                        [-9.5667e-04,  1.4299e-03,  7.3620e-03],\n",
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              "              \n",
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              "              \n",
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              "              \n",
              "              \n",
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              "              \n",
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              "              \n",
              "                       [[ 1.8118e-02,  7.9297e-03, -1.2416e-02],\n",
              "                        [-9.6234e-03,  6.5019e-03,  1.4255e-02],\n",
              "                        [ 9.4274e-03, -1.6889e-02,  2.6013e-03]],\n",
              "              \n",
              "                       ...,\n",
              "              \n",
              "                       [[ 9.5057e-04, -7.3912e-03, -1.4061e-03],\n",
              "                        [ 2.2950e-03, -1.6781e-02,  1.6179e-02],\n",
              "                        [ 1.0021e-02,  6.1912e-04,  1.0330e-02]],\n",
              "              \n",
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              "                        [ 4.2203e-03,  1.2221e-02, -2.0740e-02],\n",
              "                        [-4.3064e-03,  1.8584e-02, -1.4680e-02]],\n",
              "              \n",
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              "                        [ 1.8669e-02,  1.6321e-02,  1.0426e-02],\n",
              "                        [-1.3385e-02,  1.3662e-02, -7.3144e-03]]],\n",
              "              \n",
              "              \n",
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              "                        [-1.2590e-02,  1.6529e-02, -1.5188e-02],\n",
              "                        [-4.6064e-03, -1.1144e-03,  5.3384e-03]],\n",
              "              \n",
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              "                        [-1.2085e-02,  1.5465e-02, -1.3862e-02]],\n",
              "              \n",
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              "                        [-1.0846e-02, -6.7310e-03,  1.7042e-02]],\n",
              "              \n",
              "                       ...,\n",
              "              \n",
              "                       [[ 7.5062e-03,  1.9122e-02,  1.3978e-02],\n",
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              "                        [ 2.0229e-02, -1.0488e-02,  2.0253e-02]],\n",
              "              \n",
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              "              \n",
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              "                        [-2.0401e-02,  1.1962e-02,  4.6889e-03],\n",
              "                        [-1.6508e-02, -1.5707e-02, -7.7933e-04]]]])),\n",
              "             ('layer4.0.bn1.weight',\n",
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              "             ('layer4.0.bn1.bias',\n",
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              "             ('layer4.0.bn1.running_mean',\n",
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              "                       0.0442, -0.0234, -0.0009, -0.0885, -0.0890, -0.0180,  0.0072,  0.0064,\n",
              "                      -0.0968, -0.0271, -0.0549,  0.0025, -0.0263, -0.0447,  0.0505, -0.0218])),\n",
              "             ('layer4.0.bn1.running_var',\n",
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              "                      0.9277, 0.9263, 0.9302, 0.9341, 0.9402, 0.9364, 0.9275, 0.9254, 0.9361,\n",
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              "                      0.9449, 0.9325, 0.9443, 0.9336, 0.9430, 0.9308, 0.9311, 0.9321, 0.9314,\n",
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              "                      0.9306, 0.9320, 0.9371, 0.9302, 0.9309, 0.9342, 0.9424, 0.9330, 0.9306,\n",
              "                      0.9305, 0.9383, 0.9395, 0.9326, 0.9311, 0.9274, 0.9331, 0.9293, 0.9284,\n",
              "                      0.9270, 0.9242, 0.9451, 0.9307, 0.9335, 0.9277, 0.9249, 0.9366, 0.9389,\n",
              "                      0.9322, 0.9371, 0.9308, 0.9317, 0.9461, 0.9279, 0.9351, 0.9329, 0.9327,\n",
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              "                      0.9355, 0.9320, 0.9402, 0.9324, 0.9476, 0.9343, 0.9284, 0.9326, 0.9319,\n",
              "                      0.9277, 0.9310, 0.9304, 0.9286, 0.9349, 0.9447, 0.9276, 0.9355, 0.9316,\n",
              "                      0.9347, 0.9315, 0.9303, 0.9317, 0.9373, 0.9462, 0.9361, 0.9306, 0.9466,\n",
              "                      0.9264, 0.9348, 0.9355, 0.9270, 0.9383, 0.9320, 0.9265, 0.9310, 0.9268,\n",
              "                      0.9365, 0.9312, 0.9420, 0.9285, 0.9435, 0.9294, 0.9368, 0.9279, 0.9290,\n",
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              "                      0.9291, 0.9298, 0.9302, 0.9318, 0.9300, 0.9340, 0.9333, 0.9302, 0.9325,\n",
              "                      0.9332, 0.9292, 0.9327, 0.9302, 0.9521, 0.9287, 0.9332, 0.9500, 0.9389,\n",
              "                      0.9349, 0.9298, 0.9298, 0.9308, 0.9284, 0.9348, 0.9289, 0.9318, 0.9274,\n",
              "                      0.9282, 0.9408, 0.9291, 0.9263, 0.9571, 0.9286, 0.9267, 0.9352, 0.9387,\n",
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              "                      0.9474, 0.9299, 0.9302, 0.9416, 0.9281, 0.9359, 0.9284, 0.9288])),\n",
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              "             ('layer4.0.conv2.weight',\n",
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              "              \n",
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              "              \n",
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              "              \n",
              "                       ...,\n",
              "              \n",
              "                       [[ 1.6423e-03,  9.6250e-03, -4.9296e-03],\n",
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              "              \n",
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              "              \n",
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              "              \n",
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              "              \n",
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              "              \n",
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              "              \n",
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              "              \n",
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              "              \n",
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              "              \n",
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              "              \n",
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              "             ('layer4.0.bn2.running_var',\n",
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              "              \n",
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              "                      0.9368, 0.9427, 0.9303, 0.9342, 0.9298, 0.9324, 0.9345, 0.9451, 0.9318,\n",
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              "                      0.9309, 0.9279, 0.9320, 0.9417, 0.9331, 0.9345, 0.9291, 0.9392, 0.9282,\n",
              "                      0.9320, 0.9347, 0.9331, 0.9327, 0.9276, 0.9412, 0.9340, 0.9295, 0.9319,\n",
              "                      0.9328, 0.9355, 0.9429, 0.9300, 0.9330, 0.9291, 0.9306, 0.9324, 0.9341,\n",
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              "                      0.9455, 0.9322, 0.9365, 0.9314, 0.9356, 0.9342, 0.9317, 0.9336, 0.9333,\n",
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              "                      0.9420, 0.9393, 0.9375, 0.9384, 0.9324, 0.9313, 0.9389, 0.9375, 0.9309,\n",
              "                      0.9304, 0.9318, 0.9326, 0.9327, 0.9312, 0.9333, 0.9333, 0.9329, 0.9356,\n",
              "                      0.9328, 0.9314, 0.9375, 0.9347, 0.9319, 0.9319, 0.9329, 0.9297, 0.9434,\n",
              "                      0.9319, 0.9424, 0.9365, 0.9315, 0.9282, 0.9329, 0.9396, 0.9383, 0.9342,\n",
              "                      0.9299, 0.9344, 0.9360, 0.9315, 0.9419, 0.9464, 0.9338, 0.9268, 0.9252,\n",
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              "                      0.9402, 0.9441, 0.9333, 0.9396, 0.9368, 0.9270, 0.9401, 0.9371, 0.9339,\n",
              "                      0.9383, 0.9351, 0.9384, 0.9349, 0.9345, 0.9276, 0.9349, 0.9279, 0.9316,\n",
              "                      0.9299, 0.9318, 0.9498, 0.9262, 0.9283, 0.9338, 0.9410, 0.9371, 0.9328,\n",
              "                      0.9424, 0.9287, 0.9342, 0.9359, 0.9364, 0.9316, 0.9266, 0.9294, 0.9404,\n",
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              "                      0.9329, 0.9345, 0.9337, 0.9299, 0.9347, 0.9326, 0.9342, 0.9381, 0.9339,\n",
              "                      0.9313, 0.9320, 0.9321, 0.9302, 0.9343, 0.9333, 0.9307, 0.9318, 0.9412,\n",
              "                      0.9344, 0.9304, 0.9333, 0.9243, 0.9387, 0.9279, 0.9568, 0.9393, 0.9474,\n",
              "                      0.9460, 0.9441, 0.9378, 0.9393, 0.9307, 0.9294, 0.9306, 0.9306, 0.9265,\n",
              "                      0.9346, 0.9318, 0.9400, 0.9341, 0.9360, 0.9463, 0.9273, 0.9267, 0.9352,\n",
              "                      0.9311, 0.9309, 0.9319, 0.9379, 0.9280, 0.9347, 0.9325, 0.9320, 0.9244,\n",
              "                      0.9259, 0.9290, 0.9335, 0.9298, 0.9434, 0.9323, 0.9369, 0.9286, 0.9483,\n",
              "                      0.9295, 0.9323, 0.9342, 0.9314, 0.9367, 0.9314, 0.9392, 0.9277, 0.9319,\n",
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              "                      0.9333, 0.9386, 0.9375, 0.9315, 0.9322, 0.9360, 0.9259, 0.9361, 0.9353,\n",
              "                      0.9325, 0.9396, 0.9336, 0.9344, 0.9271, 0.9276, 0.9335, 0.9322, 0.9384,\n",
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              "                      0.9360, 0.9448, 0.9343, 0.9305, 0.9293, 0.9303, 0.9391, 0.9332, 0.9301,\n",
              "                      0.9420, 0.9344, 0.9327, 0.9322, 0.9273, 0.9300, 0.9301, 0.9305])),\n",
              "             ('layer4.0.shortcut.1.num_batches_tracked', tensor(1)),\n",
              "             ('layer4.1.conv1.weight',\n",
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              "              \n",
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              "              \n",
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              "              \n",
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              "              \n",
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              "              \n",
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              "              \n",
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              "              \n",
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              "              \n",
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              "              \n",
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              "              \n",
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              "              \n",
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              "              \n",
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              "              \n",
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              "              \n",
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              "              \n",
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              "              \n",
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              "              \n",
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              "              \n",
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              "              \n",
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              "                        [-4.6780e-04, -7.4871e-04,  1.3984e-03]]]])),\n",
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              "                      1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,\n",
              "                      1., 1., 1., 1., 1., 1., 1., 1.])),\n",
              "             ('layer4.1.bn1.bias',\n",
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              "                      0., 0., 0., 0., 0., 0., 0., 0.])),\n",
              "             ('layer4.1.bn1.running_mean',\n",
              "              tensor([-0.0261,  0.0116, -0.0370,  0.0538, -0.0147,  0.0242,  0.0143,  0.0143,\n",
              "                       0.0202,  0.0049, -0.0022,  0.0029,  0.0202, -0.0045, -0.0107, -0.0247,\n",
              "                       0.0317,  0.0123,  0.0253,  0.0092, -0.0197,  0.0004,  0.0063, -0.0040,\n",
              "                       0.0027,  0.0241, -0.0321, -0.0225,  0.0215,  0.0051, -0.0105, -0.0070,\n",
              "                      -0.0439,  0.0315,  0.0276,  0.0192, -0.0353, -0.0079, -0.0023,  0.0099,\n",
              "                       0.0163,  0.0058,  0.0155, -0.0168, -0.0379,  0.0200,  0.0106, -0.0043,\n",
              "                       0.0139,  0.0544,  0.0145,  0.0432,  0.0462, -0.0044,  0.0607,  0.0145,\n",
              "                      -0.0134,  0.0351,  0.0012, -0.0040,  0.0064,  0.0371,  0.0135, -0.0260,\n",
              "                      -0.0058, -0.0470,  0.0141,  0.0138, -0.0101,  0.0501, -0.0272,  0.0114,\n",
              "                       0.0191,  0.0236, -0.0204,  0.0172, -0.0176, -0.0092,  0.0193,  0.0247,\n",
              "                       0.0549, -0.0034, -0.0329, -0.0054, -0.0177,  0.0148,  0.0221, -0.0425,\n",
              "                      -0.0036,  0.0225,  0.0257, -0.0318, -0.0055, -0.0226, -0.0353, -0.0313,\n",
              "                       0.0011,  0.0328, -0.0194,  0.0008,  0.0250,  0.0131,  0.0142, -0.0245,\n",
              "                       0.0340, -0.0339,  0.0082, -0.0127,  0.0117,  0.0161, -0.0075, -0.0225,\n",
              "                       0.0045, -0.0551,  0.0205,  0.0343,  0.0038,  0.0128,  0.0176, -0.0290,\n",
              "                       0.0053,  0.0101,  0.0074,  0.0210,  0.0430, -0.0399,  0.0008, -0.0001,\n",
              "                      -0.0151, -0.0079,  0.0343, -0.0113, -0.0160,  0.0214, -0.0481,  0.0100,\n",
              "                      -0.0096, -0.0129, -0.0173, -0.0213,  0.0038,  0.0252,  0.0091,  0.0281,\n",
              "                       0.0111, -0.0261, -0.0079, -0.0090,  0.0247,  0.0462,  0.0348, -0.0141,\n",
              "                       0.0138,  0.0132, -0.0334,  0.0082, -0.0367, -0.0148,  0.0065,  0.0231,\n",
              "                       0.0138,  0.0146,  0.0140, -0.0072,  0.0019,  0.0190, -0.0124,  0.0300,\n",
              "                      -0.0246, -0.0321, -0.0392,  0.0089,  0.0263, -0.0048,  0.0364,  0.0040,\n",
              "                      -0.0025, -0.0004, -0.0077,  0.0282, -0.0495, -0.0130,  0.0246,  0.0157,\n",
              "                       0.0361,  0.0263, -0.0124, -0.0205, -0.0356, -0.0016, -0.0221,  0.0121,\n",
              "                      -0.0230, -0.0021, -0.0515,  0.0292,  0.0147,  0.0124, -0.0008, -0.0235,\n",
              "                       0.0333,  0.0158, -0.0014, -0.0138, -0.0178,  0.0183,  0.0559, -0.0384,\n",
              "                       0.0352, -0.0007,  0.0194,  0.0315,  0.0502, -0.0068, -0.0535,  0.0300,\n",
              "                      -0.0277,  0.0286,  0.0012, -0.0095,  0.0387, -0.0033,  0.0110,  0.0067,\n",
              "                      -0.0311, -0.0339,  0.0068, -0.0170, -0.0209, -0.0470, -0.0071,  0.0230,\n",
              "                       0.0436, -0.0035,  0.0169, -0.0015,  0.0412, -0.0149, -0.0501,  0.0184,\n",
              "                       0.0033, -0.0223, -0.0114,  0.0040,  0.0020,  0.0393, -0.0434, -0.0377,\n",
              "                       0.0096,  0.0163, -0.0325, -0.0445, -0.0255,  0.0097,  0.0124,  0.0216,\n",
              "                      -0.0196,  0.0014, -0.0054, -0.0348, -0.0077, -0.0151,  0.0070,  0.0373,\n",
              "                      -0.0309,  0.0024,  0.0010,  0.0043,  0.0123, -0.0318,  0.0195, -0.0167,\n",
              "                      -0.0217,  0.0126,  0.0167, -0.0074, -0.0081, -0.0133,  0.0121, -0.0216,\n",
              "                       0.0054,  0.0415,  0.0060, -0.0373,  0.0125, -0.0589, -0.0232,  0.0437,\n",
              "                      -0.0008,  0.0194,  0.0437,  0.0027,  0.0152, -0.0133,  0.0091,  0.0178,\n",
              "                       0.0092, -0.0224, -0.0178,  0.0044, -0.0242,  0.0141,  0.0007, -0.0344,\n",
              "                      -0.0015, -0.0111,  0.0301,  0.0373, -0.0314,  0.0139, -0.0042, -0.0075,\n",
              "                      -0.0187, -0.0265,  0.0102, -0.0043, -0.0305, -0.0169,  0.0230,  0.0088,\n",
              "                      -0.0108,  0.0593, -0.0219, -0.0290,  0.0709, -0.0360,  0.0309,  0.0172,\n",
              "                       0.0395,  0.0261, -0.0241,  0.0228, -0.0064, -0.0053, -0.0334, -0.0116,\n",
              "                      -0.0054, -0.0430,  0.0288,  0.0052,  0.0213,  0.0181, -0.0276, -0.0349,\n",
              "                      -0.0287, -0.0208,  0.0131, -0.0287, -0.0033, -0.0273,  0.0232,  0.0088,\n",
              "                       0.0125,  0.0069, -0.0027, -0.0136, -0.0185,  0.0242, -0.0084, -0.0027,\n",
              "                      -0.0261,  0.0280,  0.0061,  0.0058, -0.0351,  0.0212, -0.0083,  0.0282,\n",
              "                       0.0148,  0.0067, -0.0236,  0.0350,  0.0044, -0.0281, -0.0199,  0.0173,\n",
              "                      -0.0278, -0.0011,  0.0063,  0.0121,  0.0149, -0.0281,  0.0145,  0.0048,\n",
              "                      -0.0113, -0.0081,  0.0061, -0.0342, -0.0524, -0.0169, -0.0163,  0.0173,\n",
              "                      -0.0236,  0.0044, -0.0347,  0.0111, -0.0152, -0.0110,  0.0477, -0.0027,\n",
              "                      -0.0134, -0.0090,  0.0068, -0.0006, -0.0083, -0.0117, -0.0246,  0.0147,\n",
              "                       0.0155,  0.0098, -0.0012, -0.0170,  0.0135,  0.0038,  0.0161,  0.0023,\n",
              "                       0.0158, -0.0117, -0.0140, -0.0069, -0.0023, -0.0052,  0.0074, -0.0070,\n",
              "                       0.0150,  0.0104,  0.0394,  0.0306,  0.0336,  0.0063, -0.0053, -0.0325,\n",
              "                      -0.0215, -0.0031, -0.0119, -0.0008, -0.0209,  0.0183, -0.0280,  0.0062,\n",
              "                      -0.0051,  0.0334, -0.0166, -0.0162,  0.0059, -0.0332,  0.0186,  0.0265,\n",
              "                      -0.0035, -0.0256,  0.0456,  0.0798,  0.0183,  0.0131, -0.0007,  0.0138,\n",
              "                       0.0080,  0.0129,  0.0025, -0.0113,  0.0163, -0.0103, -0.0019, -0.0232,\n",
              "                      -0.0034, -0.0045, -0.0002,  0.0421,  0.0079,  0.0442, -0.0325,  0.0007,\n",
              "                      -0.0379, -0.0092,  0.0036, -0.0302, -0.0143, -0.0186, -0.0074,  0.0078,\n",
              "                       0.0072,  0.0400,  0.0017, -0.0125,  0.0472, -0.0165,  0.0181, -0.0299,\n",
              "                      -0.0117,  0.0091,  0.0243,  0.0124, -0.0304, -0.0131,  0.0474, -0.0307,\n",
              "                      -0.0269, -0.0148, -0.0050,  0.0142,  0.0039,  0.0224, -0.0027, -0.0080,\n",
              "                       0.0044,  0.0032, -0.0312, -0.0082, -0.0292, -0.0166, -0.0173,  0.0454])),\n",
              "             ('layer4.1.bn1.running_var',\n",
              "              tensor([0.9172, 0.9193, 0.9182, 0.9193, 0.9195, 0.9159, 0.9190, 0.9171, 0.9183,\n",
              "                      0.9176, 0.9204, 0.9168, 0.9230, 0.9159, 0.9169, 0.9180, 0.9216, 0.9169,\n",
              "                      0.9154, 0.9190, 0.9207, 0.9162, 0.9180, 0.9171, 0.9162, 0.9157, 0.9176,\n",
              "                      0.9158, 0.9178, 0.9175, 0.9159, 0.9135, 0.9201, 0.9197, 0.9189, 0.9203,\n",
              "                      0.9178, 0.9206, 0.9171, 0.9176, 0.9205, 0.9207, 0.9170, 0.9172, 0.9194,\n",
              "                      0.9175, 0.9196, 0.9171, 0.9184, 0.9224, 0.9174, 0.9192, 0.9166, 0.9192,\n",
              "                      0.9223, 0.9195, 0.9177, 0.9234, 0.9189, 0.9163, 0.9177, 0.9242, 0.9172,\n",
              "                      0.9268, 0.9172, 0.9187, 0.9180, 0.9181, 0.9149, 0.9189, 0.9142, 0.9211,\n",
              "                      0.9192, 0.9214, 0.9177, 0.9222, 0.9150, 0.9169, 0.9191, 0.9166, 0.9241,\n",
              "                      0.9183, 0.9207, 0.9164, 0.9180, 0.9167, 0.9157, 0.9188, 0.9152, 0.9203,\n",
              "                      0.9171, 0.9174, 0.9150, 0.9169, 0.9164, 0.9159, 0.9187, 0.9209, 0.9163,\n",
              "                      0.9266, 0.9170, 0.9230, 0.9213, 0.9196, 0.9239, 0.9153, 0.9175, 0.9192,\n",
              "                      0.9175, 0.9197, 0.9184, 0.9166, 0.9165, 0.9221, 0.9225, 0.9209, 0.9152,\n",
              "                      0.9179, 0.9176, 0.9219, 0.9186, 0.9167, 0.9208, 0.9192, 0.9139, 0.9180,\n",
              "                      0.9154, 0.9171, 0.9232, 0.9191, 0.9202, 0.9152, 0.9159, 0.9162, 0.9202,\n",
              "                      0.9232, 0.9197, 0.9174, 0.9180, 0.9198, 0.9168, 0.9188, 0.9242, 0.9169,\n",
              "                      0.9193, 0.9161, 0.9178, 0.9197, 0.9190, 0.9219, 0.9200, 0.9203, 0.9154,\n",
              "                      0.9235, 0.9163, 0.9165, 0.9272, 0.9174, 0.9196, 0.9168, 0.9170, 0.9167,\n",
              "                      0.9153, 0.9219, 0.9168, 0.9162, 0.9196, 0.9187, 0.9171, 0.9194, 0.9306,\n",
              "                      0.9206, 0.9168, 0.9168, 0.9188, 0.9210, 0.9163, 0.9141, 0.9181, 0.9155,\n",
              "                      0.9179, 0.9159, 0.9207, 0.9176, 0.9227, 0.9211, 0.9260, 0.9200, 0.9184,\n",
              "                      0.9174, 0.9271, 0.9170, 0.9211, 0.9196, 0.9246, 0.9175, 0.9182, 0.9191,\n",
              "                      0.9165, 0.9176, 0.9183, 0.9179, 0.9212, 0.9172, 0.9166, 0.9183, 0.9381,\n",
              "                      0.9217, 0.9182, 0.9221, 0.9155, 0.9190, 0.9292, 0.9177, 0.9182, 0.9205,\n",
              "                      0.9192, 0.9236, 0.9145, 0.9181, 0.9148, 0.9176, 0.9184, 0.9188, 0.9251,\n",
              "                      0.9215, 0.9197, 0.9204, 0.9176, 0.9235, 0.9188, 0.9281, 0.9225, 0.9160,\n",
              "                      0.9155, 0.9196, 0.9221, 0.9210, 0.9213, 0.9189, 0.9201, 0.9187, 0.9156,\n",
              "                      0.9159, 0.9183, 0.9184, 0.9210, 0.9193, 0.9175, 0.9188, 0.9206, 0.9255,\n",
              "                      0.9187, 0.9224, 0.9193, 0.9204, 0.9145, 0.9169, 0.9178, 0.9224, 0.9187,\n",
              "                      0.9163, 0.9190, 0.9168, 0.9187, 0.9236, 0.9201, 0.9191, 0.9215, 0.9195,\n",
              "                      0.9216, 0.9176, 0.9181, 0.9161, 0.9167, 0.9179, 0.9157, 0.9172, 0.9170,\n",
              "                      0.9162, 0.9194, 0.9197, 0.9179, 0.9200, 0.9202, 0.9210, 0.9217, 0.9217,\n",
              "                      0.9191, 0.9255, 0.9196, 0.9156, 0.9166, 0.9153, 0.9143, 0.9195, 0.9211,\n",
              "                      0.9165, 0.9257, 0.9156, 0.9210, 0.9222, 0.9194, 0.9216, 0.9200, 0.9209,\n",
              "                      0.9173, 0.9247, 0.9197, 0.9184, 0.9189, 0.9217, 0.9186, 0.9202, 0.9223,\n",
              "                      0.9174, 0.9212, 0.9153, 0.9186, 0.9254, 0.9206, 0.9258, 0.9168, 0.9190,\n",
              "                      0.9233, 0.9212, 0.9165, 0.9186, 0.9178, 0.9185, 0.9153, 0.9210, 0.9167,\n",
              "                      0.9164, 0.9249, 0.9158, 0.9181, 0.9178, 0.9168, 0.9166, 0.9196, 0.9177,\n",
              "                      0.9174, 0.9199, 0.9185, 0.9230, 0.9213, 0.9177, 0.9174, 0.9187, 0.9174,\n",
              "                      0.9181, 0.9214, 0.9192, 0.9184, 0.9172, 0.9167, 0.9169, 0.9238, 0.9176,\n",
              "                      0.9180, 0.9154, 0.9151, 0.9168, 0.9239, 0.9191, 0.9226, 0.9165, 0.9177,\n",
              "                      0.9164, 0.9198, 0.9250, 0.9171, 0.9212, 0.9243, 0.9153, 0.9276, 0.9174,\n",
              "                      0.9170, 0.9189, 0.9194, 0.9210, 0.9209, 0.9171, 0.9151, 0.9176, 0.9157,\n",
              "                      0.9208, 0.9261, 0.9167, 0.9210, 0.9211, 0.9156, 0.9157, 0.9221, 0.9167,\n",
              "                      0.9170, 0.9169, 0.9174, 0.9162, 0.9174, 0.9255, 0.9153, 0.9264, 0.9182,\n",
              "                      0.9191, 0.9262, 0.9207, 0.9158, 0.9163, 0.9190, 0.9203, 0.9178, 0.9170,\n",
              "                      0.9161, 0.9265, 0.9159, 0.9215, 0.9211, 0.9178, 0.9173, 0.9171, 0.9168,\n",
              "                      0.9230, 0.9193, 0.9168, 0.9224, 0.9185, 0.9193, 0.9190, 0.9159, 0.9190,\n",
              "                      0.9182, 0.9195, 0.9185, 0.9187, 0.9225, 0.9199, 0.9162, 0.9184, 0.9184,\n",
              "                      0.9225, 0.9168, 0.9165, 0.9189, 0.9169, 0.9201, 0.9170, 0.9162, 0.9211,\n",
              "                      0.9210, 0.9311, 0.9217, 0.9241, 0.9187, 0.9164, 0.9163, 0.9197, 0.9168,\n",
              "                      0.9172, 0.9160, 0.9203, 0.9255, 0.9190, 0.9157, 0.9165, 0.9168, 0.9191,\n",
              "                      0.9182, 0.9208, 0.9203, 0.9177, 0.9161, 0.9194, 0.9196, 0.9183, 0.9242,\n",
              "                      0.9171, 0.9198, 0.9141, 0.9187, 0.9212, 0.9179, 0.9178, 0.9200, 0.9177,\n",
              "                      0.9205, 0.9153, 0.9250, 0.9174, 0.9176, 0.9224, 0.9174, 0.9152, 0.9184,\n",
              "                      0.9177, 0.9210, 0.9169, 0.9170, 0.9172, 0.9155, 0.9216, 0.9201, 0.9161,\n",
              "                      0.9177, 0.9200, 0.9168, 0.9256, 0.9184, 0.9197, 0.9179, 0.9227])),\n",
              "             ('layer4.1.bn1.num_batches_tracked', tensor(1)),\n",
              "             ('layer4.1.conv2.weight',\n",
              "              tensor([[[[-2.7084e-03,  7.0422e-03, -8.6837e-03],\n",
              "                        [ 5.6632e-03, -1.7812e-03, -1.4488e-02],\n",
              "                        [ 4.5886e-03, -6.9379e-03, -1.0973e-03]],\n",
              "              \n",
              "                       [[-1.1067e-02, -1.3999e-02, -1.1469e-02],\n",
              "                        [-1.1461e-02, -9.6551e-03, -7.8384e-03],\n",
              "                        [-7.0344e-03,  1.2919e-02,  7.1468e-03]],\n",
              "              \n",
              "                       [[-8.1254e-03,  7.1355e-03,  4.5750e-03],\n",
              "                        [-1.1967e-02, -1.3911e-02, -1.8911e-03],\n",
              "                        [-1.1896e-02, -5.7708e-03,  1.1585e-02]],\n",
              "              \n",
              "                       ...,\n",
              "              \n",
              "                       [[-4.9073e-03,  6.3756e-03, -2.9995e-03],\n",
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              "              \n",
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              "              \n",
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              "              \n",
              "              \n",
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              "              \n",
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              "              \n",
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              "                        [-1.3651e-02,  8.3167e-03, -2.2327e-03]],\n",
              "              \n",
              "                       ...,\n",
              "              \n",
              "                       [[-1.2888e-02, -3.0001e-03,  2.1968e-03],\n",
              "                        [-7.8290e-03,  1.3575e-03,  5.3468e-03],\n",
              "                        [-1.1704e-02,  4.1042e-03, -7.0010e-03]],\n",
              "              \n",
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              "              \n",
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              "              \n",
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              "              \n",
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              "              \n",
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              "              \n",
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              "              \n",
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              "              \n",
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              "              \n",
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              "              \n",
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              "              \n",
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              "              \n",
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              "              \n",
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              "              \n",
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              "             ('layer4.1.bn2.weight',\n",
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              "                      -4.1111e-03,  3.5008e-03, -2.8869e-02,  4.2099e-03, -6.2916e-03,\n",
              "                       2.8453e-02, -1.8900e-02, -1.0255e-02, -2.7313e-02, -1.9279e-02,\n",
              "                      -1.4259e-02,  2.3301e-02, -2.6754e-02,  5.9948e-03,  3.8035e-03,\n",
              "                      -1.2096e-02,  5.3871e-03, -6.5420e-03,  8.5331e-03, -2.9558e-03,\n",
              "                       1.6722e-02, -9.6431e-03,  1.5758e-03, -4.0179e-02, -1.6502e-02,\n",
              "                       2.6005e-02,  2.9302e-03, -5.3032e-04, -9.1675e-04, -1.6233e-02,\n",
              "                       3.6198e-03,  6.9253e-03, -2.1415e-02, -8.8299e-03, -8.2528e-03,\n",
              "                      -1.2436e-03,  4.5655e-03,  2.0623e-02, -1.9512e-02, -1.1980e-02,\n",
              "                      -7.9234e-03,  3.3439e-02, -2.8392e-02,  5.1853e-04, -5.5317e-03,\n",
              "                      -9.1948e-03,  4.6480e-03,  9.2440e-04, -1.7471e-03,  5.2509e-04,\n",
              "                       1.5162e-02,  7.7672e-03, -1.6787e-02, -1.9723e-02, -1.2042e-02,\n",
              "                      -1.4450e-02, -2.4463e-02,  3.1335e-03, -4.5066e-03,  6.3504e-04,\n",
              "                      -3.6662e-04, -1.4945e-02,  1.7509e-02,  1.2169e-02, -1.4959e-02,\n",
              "                       3.2235e-02,  1.0341e-02,  5.4180e-03,  1.7463e-02, -1.7038e-02,\n",
              "                      -1.3272e-02,  1.3001e-02, -1.9674e-02,  2.4304e-02, -3.2244e-02,\n",
              "                      -1.4502e-03,  2.0643e-02,  2.9269e-02, -1.2541e-02,  1.7236e-02,\n",
              "                       9.7668e-03,  1.4626e-02, -2.6857e-02, -2.2125e-02, -6.9483e-03,\n",
              "                      -2.2916e-02, -6.4148e-04,  1.4726e-03, -1.8562e-02, -2.5632e-02,\n",
              "                      -4.4966e-03, -2.9635e-03, -8.5887e-03, -2.3311e-03,  1.3393e-02,\n",
              "                       9.1365e-03,  1.1124e-02,  1.0510e-03, -1.3484e-02, -3.9473e-03,\n",
              "                      -3.2690e-03, -9.5648e-03, -2.7207e-02,  3.1456e-02,  2.8878e-02,\n",
              "                      -5.7716e-03, -1.0931e-02, -1.8283e-02,  1.3567e-02, -1.7497e-02,\n",
              "                      -1.5653e-02,  2.0517e-04,  2.0072e-02,  2.0878e-02, -8.4432e-03,\n",
              "                       1.8437e-02, -3.1790e-02,  9.4813e-03,  4.8559e-02, -1.0155e-02,\n",
              "                      -2.2931e-02,  6.7358e-03,  2.3400e-02,  2.1295e-02, -3.2598e-03,\n",
              "                      -5.5985e-03,  3.3126e-02,  2.9536e-02, -5.9282e-03, -2.5400e-03,\n",
              "                      -1.2978e-02,  1.8434e-02,  1.3990e-02, -1.2484e-02, -2.4214e-02,\n",
              "                      -2.7207e-02,  1.0528e-02, -1.6791e-02,  1.8730e-02,  3.7301e-02,\n",
              "                      -1.5196e-02, -1.8319e-02, -8.1938e-03,  7.5029e-04, -5.2895e-03,\n",
              "                      -1.1758e-03,  3.7462e-03, -1.3542e-03,  4.2076e-02, -2.6167e-03,\n",
              "                      -1.5139e-02,  1.2047e-02, -1.5852e-02, -1.7137e-03,  7.4184e-03,\n",
              "                       2.8293e-02,  1.7065e-03, -2.0077e-02,  7.0701e-03,  4.7074e-03,\n",
              "                      -9.2333e-03, -1.2108e-02, -7.2532e-03, -3.0582e-02, -3.9529e-03,\n",
              "                       1.0560e-02, -5.3277e-03, -1.6374e-03, -1.1503e-02,  6.6053e-03,\n",
              "                       2.9011e-02,  2.2876e-02, -5.2964e-03, -3.5233e-03,  1.2711e-02,\n",
              "                      -1.3468e-02,  1.9303e-02,  6.1950e-03, -2.2342e-02, -3.1808e-02,\n",
              "                       2.1134e-02,  5.7683e-03, -1.5279e-02,  2.1961e-03,  1.4435e-02,\n",
              "                       7.8217e-03,  6.5135e-03,  3.7308e-04, -6.0863e-03, -2.3492e-02,\n",
              "                       2.4516e-02,  8.0123e-04, -9.6088e-03,  4.1757e-03,  2.0281e-02,\n",
              "                      -9.5613e-03, -1.4252e-02,  5.1849e-03,  7.5725e-04,  9.3924e-03,\n",
              "                       5.0463e-04,  7.0035e-03,  5.4662e-03,  1.1922e-02,  1.9142e-02,\n",
              "                      -3.1906e-03, -2.7384e-02, -1.5297e-02,  1.8870e-02,  2.2557e-02,\n",
              "                      -5.5056e-03, -2.5086e-03,  2.4167e-02,  4.1546e-03, -1.0062e-02,\n",
              "                       1.1033e-02, -1.1800e-02,  2.6281e-02, -1.7739e-02,  6.6992e-03,\n",
              "                      -2.5648e-02, -8.2559e-03,  1.4191e-02, -4.5585e-03, -1.2835e-02,\n",
              "                       1.8623e-03,  1.2469e-02,  3.0272e-02,  2.1977e-04,  2.2358e-02,\n",
              "                       6.7794e-04,  1.7385e-02,  7.7119e-03, -9.7280e-03,  1.3341e-02,\n",
              "                       1.3121e-03, -3.3035e-02,  6.3632e-04,  2.8770e-03, -4.8456e-03,\n",
              "                       3.9544e-03,  2.4930e-02,  2.9229e-02,  8.4805e-03, -4.4024e-03,\n",
              "                      -6.3021e-03,  1.1447e-02, -2.4115e-02,  4.5725e-04,  1.7042e-02,\n",
              "                      -1.6220e-02,  5.1549e-03,  1.4096e-02,  2.6951e-02, -1.8885e-02,\n",
              "                       1.8605e-02,  2.6795e-02, -2.0222e-02,  2.6930e-03,  9.5880e-03,\n",
              "                      -9.7324e-03,  1.6096e-02,  3.1802e-02, -1.3068e-02,  1.1334e-02,\n",
              "                      -5.7455e-03,  6.5195e-03,  1.8503e-02,  3.1358e-03, -1.5452e-02,\n",
              "                       2.1369e-02, -2.5237e-02,  1.3242e-02, -1.7884e-02, -1.7979e-03,\n",
              "                       1.7937e-02,  3.9898e-02, -4.6597e-03, -1.9028e-02, -1.9612e-03,\n",
              "                       1.3623e-02,  3.8543e-03,  1.6731e-03,  1.8461e-02,  3.9883e-02,\n",
              "                      -1.2149e-02,  1.2132e-02, -3.0686e-02, -1.8615e-02,  1.1077e-02,\n",
              "                      -1.0843e-03, -1.0129e-02,  1.4921e-02,  1.0832e-02,  1.8225e-03,\n",
              "                       1.3670e-02,  1.2337e-02, -1.9283e-02, -3.2871e-03,  2.1654e-02,\n",
              "                      -8.7332e-03,  5.9407e-03, -2.5355e-02, -6.1816e-03, -2.5007e-02,\n",
              "                      -2.2108e-02, -3.1795e-02,  1.0497e-02, -5.3984e-04,  7.7462e-03,\n",
              "                      -1.0716e-02,  1.7543e-02,  3.2824e-03,  1.4132e-02,  3.1637e-02,\n",
              "                       1.1539e-03, -3.6464e-03,  1.2172e-03, -1.6498e-03,  2.9129e-02,\n",
              "                      -2.6234e-02,  3.0962e-02, -1.2688e-02,  2.3065e-03, -4.9018e-03,\n",
              "                      -1.1008e-02,  3.6053e-02, -1.1775e-02, -4.2124e-03, -5.4779e-03,\n",
              "                      -1.3572e-02,  1.4341e-02, -2.5508e-03,  2.9458e-03,  2.0509e-03,\n",
              "                       2.4911e-02, -1.6194e-02,  4.0435e-03, -1.2805e-02, -1.8199e-02,\n",
              "                      -4.6104e-03, -2.4650e-02,  1.4773e-03, -3.5365e-02,  1.6724e-02,\n",
              "                      -3.4353e-02, -2.2102e-02])),\n",
              "             ('layer4.1.bn2.running_var',\n",
              "              tensor([0.9094, 0.9135, 0.9078, 0.9076, 0.9091, 0.9105, 0.9089, 0.9088, 0.9118,\n",
              "                      0.9087, 0.9094, 0.9138, 0.9080, 0.9073, 0.9087, 0.9087, 0.9118, 0.9088,\n",
              "                      0.9119, 0.9100, 0.9090, 0.9081, 0.9132, 0.9099, 0.9094, 0.9097, 0.9121,\n",
              "                      0.9092, 0.9130, 0.9095, 0.9084, 0.9118, 0.9080, 0.9098, 0.9102, 0.9080,\n",
              "                      0.9109, 0.9073, 0.9093, 0.9089, 0.9078, 0.9078, 0.9099, 0.9096, 0.9085,\n",
              "                      0.9104, 0.9139, 0.9082, 0.9089, 0.9094, 0.9090, 0.9097, 0.9095, 0.9084,\n",
              "                      0.9114, 0.9087, 0.9097, 0.9092, 0.9080, 0.9082, 0.9078, 0.9090, 0.9087,\n",
              "                      0.9106, 0.9085, 0.9129, 0.9080, 0.9107, 0.9123, 0.9091, 0.9095, 0.9115,\n",
              "                      0.9115, 0.9101, 0.9106, 0.9084, 0.9110, 0.9102, 0.9098, 0.9088, 0.9086,\n",
              "                      0.9089, 0.9104, 0.9086, 0.9090, 0.9116, 0.9132, 0.9116, 0.9102, 0.9081,\n",
              "                      0.9085, 0.9098, 0.9088, 0.9087, 0.9098, 0.9111, 0.9099, 0.9083, 0.9096,\n",
              "                      0.9096, 0.9085, 0.9095, 0.9092, 0.9084, 0.9086, 0.9097, 0.9128, 0.9086,\n",
              "                      0.9120, 0.9123, 0.9094, 0.9089, 0.9088, 0.9104, 0.9120, 0.9101, 0.9099,\n",
              "                      0.9135, 0.9076, 0.9073, 0.9103, 0.9086, 0.9077, 0.9115, 0.9093, 0.9138,\n",
              "                      0.9117, 0.9091, 0.9091, 0.9094, 0.9174, 0.9100, 0.9095, 0.9115, 0.9138,\n",
              "                      0.9080, 0.9106, 0.9100, 0.9106, 0.9083, 0.9088, 0.9109, 0.9090, 0.9100,\n",
              "                      0.9074, 0.9101, 0.9098, 0.9110, 0.9189, 0.9092, 0.9088, 0.9092, 0.9116,\n",
              "                      0.9088, 0.9114, 0.9094, 0.9091, 0.9091, 0.9089, 0.9079, 0.9094, 0.9078,\n",
              "                      0.9099, 0.9079, 0.9097, 0.9140, 0.9076, 0.9088, 0.9087, 0.9084, 0.9097,\n",
              "                      0.9091, 0.9119, 0.9100, 0.9104, 0.9094, 0.9098, 0.9153, 0.9093, 0.9080,\n",
              "                      0.9130, 0.9080, 0.9092, 0.9123, 0.9088, 0.9119, 0.9078, 0.9082, 0.9100,\n",
              "                      0.9102, 0.9100, 0.9092, 0.9079, 0.9086, 0.9075, 0.9090, 0.9102, 0.9094,\n",
              "                      0.9070, 0.9090, 0.9088, 0.9127, 0.9094, 0.9098, 0.9088, 0.9088, 0.9105,\n",
              "                      0.9108, 0.9080, 0.9086, 0.9080, 0.9087, 0.9085, 0.9127, 0.9108, 0.9104,\n",
              "                      0.9106, 0.9134, 0.9103, 0.9108, 0.9160, 0.9091, 0.9138, 0.9093, 0.9087,\n",
              "                      0.9083, 0.9095, 0.9117, 0.9125, 0.9173, 0.9124, 0.9088, 0.9081, 0.9103,\n",
              "                      0.9103, 0.9074, 0.9092, 0.9105, 0.9077, 0.9115, 0.9091, 0.9094, 0.9102,\n",
              "                      0.9119, 0.9089, 0.9112, 0.9115, 0.9088, 0.9075, 0.9097, 0.9101, 0.9166,\n",
              "                      0.9081, 0.9103, 0.9102, 0.9094, 0.9090, 0.9098, 0.9074, 0.9135, 0.9095,\n",
              "                      0.9085, 0.9121, 0.9101, 0.9082, 0.9088, 0.9088, 0.9121, 0.9105, 0.9114,\n",
              "                      0.9081, 0.9090, 0.9100, 0.9137, 0.9091, 0.9101, 0.9077, 0.9093, 0.9103,\n",
              "                      0.9125, 0.9091, 0.9122, 0.9085, 0.9125, 0.9099, 0.9078, 0.9084, 0.9143,\n",
              "                      0.9094, 0.9098, 0.9083, 0.9115, 0.9101, 0.9092, 0.9094, 0.9137, 0.9135,\n",
              "                      0.9101, 0.9095, 0.9089, 0.9098, 0.9112, 0.9082, 0.9112, 0.9119, 0.9106,\n",
              "                      0.9087, 0.9084, 0.9089, 0.9093, 0.9094, 0.9095, 0.9094, 0.9095, 0.9104,\n",
              "                      0.9103, 0.9107, 0.9080, 0.9083, 0.9115, 0.9112, 0.9116, 0.9099, 0.9107,\n",
              "                      0.9094, 0.9107, 0.9076, 0.9086, 0.9104, 0.9097, 0.9100, 0.9092, 0.9077,\n",
              "                      0.9081, 0.9089, 0.9103, 0.9098, 0.9077, 0.9076, 0.9093, 0.9104, 0.9111,\n",
              "                      0.9079, 0.9114, 0.9099, 0.9084, 0.9114, 0.9078, 0.9095, 0.9087, 0.9083,\n",
              "                      0.9097, 0.9094, 0.9097, 0.9121, 0.9099, 0.9079, 0.9118, 0.9107, 0.9096,\n",
              "                      0.9113, 0.9085, 0.9086, 0.9094, 0.9084, 0.9097, 0.9089, 0.9107, 0.9081,\n",
              "                      0.9105, 0.9148, 0.9140, 0.9093, 0.9136, 0.9092, 0.9089, 0.9087, 0.9104,\n",
              "                      0.9078, 0.9115, 0.9083, 0.9087, 0.9113, 0.9100, 0.9096, 0.9089, 0.9120,\n",
              "                      0.9099, 0.9087, 0.9097, 0.9085, 0.9088, 0.9098, 0.9093, 0.9094, 0.9081,\n",
              "                      0.9089, 0.9099, 0.9082, 0.9102, 0.9085, 0.9097, 0.9105, 0.9108, 0.9090,\n",
              "                      0.9101, 0.9095, 0.9079, 0.9099, 0.9090, 0.9093, 0.9107, 0.9098, 0.9093,\n",
              "                      0.9077, 0.9080, 0.9085, 0.9086, 0.9099, 0.9097, 0.9095, 0.9098, 0.9084,\n",
              "                      0.9079, 0.9098, 0.9123, 0.9100, 0.9100, 0.9086, 0.9085, 0.9103, 0.9083,\n",
              "                      0.9099, 0.9089, 0.9089, 0.9104, 0.9112, 0.9088, 0.9086, 0.9084, 0.9083,\n",
              "                      0.9129, 0.9098, 0.9088, 0.9097, 0.9093, 0.9078, 0.9096, 0.9094, 0.9186,\n",
              "                      0.9074, 0.9084, 0.9109, 0.9116, 0.9083, 0.9087, 0.9089, 0.9083, 0.9105,\n",
              "                      0.9099, 0.9087, 0.9086, 0.9087, 0.9083, 0.9088, 0.9134, 0.9108, 0.9110,\n",
              "                      0.9101, 0.9101, 0.9083, 0.9143, 0.9082, 0.9120, 0.9130, 0.9102, 0.9082,\n",
              "                      0.9085, 0.9123, 0.9089, 0.9093, 0.9089, 0.9080, 0.9081, 0.9099, 0.9117,\n",
              "                      0.9097, 0.9087, 0.9085, 0.9094, 0.9123, 0.9092, 0.9101, 0.9080, 0.9104,\n",
              "                      0.9091, 0.9078, 0.9106, 0.9110, 0.9086, 0.9100, 0.9082, 0.9104, 0.9085,\n",
              "                      0.9124, 0.9087, 0.9145, 0.9107, 0.9099, 0.9095, 0.9110, 0.9087])),\n",
              "             ('layer4.1.bn2.num_batches_tracked', tensor(1)),\n",
              "             ('fc.weight',\n",
              "              tensor([[ 0.0142, -0.0090,  0.0047,  ...,  0.0082, -0.0241, -0.0291],\n",
              "                      [ 0.0288,  0.0323, -0.0276,  ..., -0.0248, -0.0311, -0.0176],\n",
              "                      [-0.0070, -0.0344,  0.0062,  ...,  0.0334, -0.0428,  0.0328],\n",
              "                      ...,\n",
              "                      [ 0.0235, -0.0378,  0.0149,  ..., -0.0396, -0.0375, -0.0236],\n",
              "                      [ 0.0325,  0.0190,  0.0107,  ..., -0.0239,  0.0047,  0.0318],\n",
              "                      [ 0.0171, -0.0078,  0.0137,  ...,  0.0113, -0.0357, -0.0063]])),\n",
              "             ('fc.bias',\n",
              "              tensor([-0.0123, -0.0173,  0.0387, -0.0368,  0.0192, -0.0415, -0.0158,  0.0212,\n",
              "                      -0.0259,  0.0423]))])"
            ]
          },
          "execution_count": 17,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "model.state_dict()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wHD02aNt4pNv"
      },
      "source": [
        "# 设置交叉熵损失函数，SGD优化器"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "ExecuteTime": {
          "end_time": "2025-06-26T01:43:40.023837Z",
          "start_time": "2025-06-26T01:43:40.019952Z"
        },
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "J1dvP3ES4pNv",
        "outputId": "2f70f03d-04c4-4a6b-c3a9-76ca31d98b6f"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "损失函数: CrossEntropyLoss()\n"
          ]
        }
      ],
      "source": [
        "model = ResNet18()\n",
        "# 定义损失函数和优化器\n",
        "loss_fn = nn.CrossEntropyLoss()  # 交叉熵损失函数，适用于多分类问题，里边会做softmax，还有会把0-9标签转换成one-hot编码\n",
        "\n",
        "print(\"损失函数:\", loss_fn)\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "ExecuteTime": {
          "end_time": "2025-06-26T01:43:40.035848Z",
          "start_time": "2025-06-26T01:43:40.032419Z"
        },
        "id": "qUeLZMIE4pNv"
      },
      "outputs": [],
      "source": [
        "model = ResNet18()\n",
        "\n",
        "optimizer = torch.optim.SGD(model.parameters(), lr=0.001, momentum=0.9)  # SGD优化器，学习率为0.01，动量为0.9"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {
        "ExecuteTime": {
          "end_time": "2025-06-26T01:45:37.732814Z",
          "start_time": "2025-06-26T01:43:40.035848Z"
        },
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 125,
          "referenced_widgets": [
            "c808a314db524051bd394e6a13382fde",
            "3b6654a81c0d475ebff94345b900ff8a",
            "de28f2664a444e52a93a5e1252fa5c68",
            "9d0bdffdd4f04468bcb8f4cdc8ded054",
            "82d88c6da0b04d1c949553269a140cb7",
            "9ccda79a54dd47669c1090d53e1bd7f5",
            "962c515f49664578b1e8de865d253fd5",
            "b3e0797dd8704682920ada7342e8afcb",
            "d6bd992709bd4cc8af1e97c5c8bd5cb9",
            "7324bc7bb10d4b45adb203dc8eab7e33",
            "50c8aac8ee444c25b5dca033e039ce97"
          ]
        },
        "id": "qI1L-GG94pNv",
        "outputId": "a8960b1e-8d78-44d6-e05d-1c1bd0e5bedc"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "使用设备: cuda:0\n",
            "训练开始，共35200步\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "c808a314db524051bd394e6a13382fde",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "  0%|          | 0/35200 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "早停触发! 最佳验证准确率(如果是回归，这里是损失): 79.6600\n",
            "早停: 在13500 步\n"
          ]
        }
      ],
      "source": [
        "device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n",
        "print(f\"使用设备: {device}\")\n",
        "model = model.to(device) #将模型移动到GPU\n",
        "early_stopping=EarlyStopping(patience=5, delta=0.001)\n",
        "model_saver=ModelSaver(save_dir='model_weights', save_best_only=True)\n",
        "\n",
        "\n",
        "model, history = train_classification_model(model, train_loader, val_loader, loss_fn, optimizer, device, num_epochs=50, early_stopping=early_stopping, model_saver=model_saver, tensorboard_logger=None)\n",
        "\n"
      ]
    },
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          "end_time": "2025-06-26T01:45:37.737721Z",
          "start_time": "2025-06-26T01:45:37.732814Z"
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        "colab": {
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        "id": "pJWn5FRH4pNv",
        "outputId": "857760e7-d5f3-4e35-f011-aac19d627e2f"
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      "outputs": [
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        "history['train'][-100:-1]"
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      "metadata": {
        "ExecuteTime": {
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        "colab": {
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        "outputId": "be4984ee-cc62-4141-d0b3-0db2f68bcd66"
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      "outputs": [
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      ],
      "source": [
        "history['val'][-1000:-1]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NcujMCRC4pNw"
      },
      "source": [
        "# 绘制损失曲线和准确率曲线"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "metadata": {
        "ExecuteTime": {
          "end_time": "2025-06-26T01:45:37.816716Z",
          "start_time": "2025-06-26T01:45:37.744941Z"
        },
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 465
        },
        "id": "3xZ57j-C4pNw",
        "outputId": "b536892c-585f-4046-f736-909a99becb60"
      },
      "outputs": [
        {
          "data": {
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tmc0rdY7YcAc3nd1KSY74Lb9IdAoCIgFwOQ+ZHImI+KvbbruNsLCwEy633Xab2eGJSCX9uCWDu6b9itvj5bJeTXlodAeGJHn565DWADz27e98srxikyJ+vz6N2evTCLBamHxpV2xWTeQpUhkNv+saUBgYCYXgzlWiIyLmePTRR7n//vtP+FxlJpwUEfMt33mYm99fQZHbw6guCTx1WVe8HqM1Z/yQVuQXe3jjxx38/cu1BAcGcGH301f1yylw8a+v1gNwy6BWdEzU3weRyvKLRKfYEQ05QP4Rs0MRET8VFxdHXFyc2WGI+L0Cl5v1+7Lp3jSSAFvlO7as3ZPFDe8sp8DlYXD7WF64uicBNiuukkTHYrHw4OiO5Ba5mbY0lQmfrCbEbmNYp/hTnveZ7zeRll1A80Yh/LWS43tExOAXXde8QcZs4LYCJToiIiL+7N5PVnPZaz8zcsoivlu7/5QV/E5m84Ecrnt7KTmFxfRrGcPUa3sTGHD8LZXFYuHxi7pwcY8kij1e7pi2isVbTz5eeFXqEd7/ZRcAT17SlSC7JvAUqQq/SHQsJSWmAwozzQ1ERERETLNhfzbfrUsDYFtGLrd/uIqLXlnMj1syyp3w7DqUy7VvLuVInovuTSN58/o+p0xIrFYLz1zRnRGd4ikq9nDTeytYuevwcfu53B4mfrEWrxcu69WUgW0aV+5NioiPXyQ6tjAj0XG4skyORERERMzy2oJtAAzvFM9fh7YlNNDGb3uy+PNby7jmjV9YlXrqnh/7MvP50xtLSc8ppENCOO/dcAbhQfbTvm6AzcpLf+rJ2W0bk+9yM+6d5azbW/ae5PVF29l0IIeY0ED+cX7DmNxXxGx+kegEhhnfioS4a2+yPxEREak7dh3K5dvf9gFwz7C2TBjejoUPDOGGgS0JtFn5ZfthLn31Z256bwWb0nKOOz4jp5Br31zK3sx8WjYO5YMb+xEVElju13cE2Hj9z33o2yKanIJirnt7GVvTjdfZeTCXF+ZtAeChCzoSE1r+84rIyflFohMUaSQ6YZ7sSvXFFRERkfpt6sLteLwwpH0snZOMaScahzl4eEwnfvjbYK7s0xSrBeZuOMB5Lyzi3k9Wk3ooD4CsPBd/fmsp2w/m0iQqmP/d1I/YcEeFYwgOtPHWuL50bRLJ4dwixr65lNRDefxjxlqKij2c3bYxF/doUq3vW8Sf+UWiExphJDqROMktcpscjYjUZy1atGDKlCnl2tdisTBjxowajUdETu9AdgFfrNwDwB1D2hz3fJOoYJ6+vDtz7j2H0V0T8Hph+q97GfrcAh6asY7r31nGxrQcYsMdfHhTP5pEBVc6loggO+/fcAbt4sM4kF3I+S/9yOKthwiyW3ni4q5YLJozR6S6+EWiE1iS6ESTw5HcIpOjERERkdr05o/bKXJ7OKNFDH1bxJx0vzZxYbw6tjffjD+Ls9s2xuX28sEvu1i9O5OoEDv/u7EfLRqHVjme6NBA/ndjP5o3CiGnoBiAe4a1o1mjkCqfW0SO8otEh2Djj1qopZCsHKfJwYiIiEhtycwr4sOlqQDcPqR1uY7p2jSSD27sx0c3n0nfFtEkRgbx/g1n0D4hvNriiosI4sOb+tG1SSTndojjxrNaVtu5RcTgH4lOUARujKZg55F0k4MRaYC8XijKrd7FlVe+/Sow7u71118nKSkJj8dTZvtFF13EDTfcwLZt27jooouIj48nLCyMvn37Mnfu3Gr7Na1du5Zzzz2X4OBgGjVqxC233ILTefTLlwULFnDGGWcQGhpKVFQUAwcOZNcuY06NNWvWMGTIEMLDw4mIiKB3796sWLGi2mITaaje/XkneUVuOiVGMLhdbIWO7d+6EZ/dNoAlE4fSrWlUtcfWNDqEb+46i7eu74O9CpOXisiJBZgdQK2wWMkhjChyyMvKMDsakYbHlQdPJlXb6axAVHl3fnAfBJavK8kVV1zBXXfdxQ8//MDQoUMBOHz4MLNnz2bWrFk4nU5Gjx7NE088gcPh4P3332fMmDFs2rSJZs2aVeat+OTm5jJy5Ej69+/P8uXLSU9P56abbmL8+PG8++67FBcXc/HFF3PzzTfz0UcfUVRUxLJly3z99ceOHUvPnj157bXXsNlsrF69Grv99GVtRfxZbmEx7/68E4A7hrSus+Nf6mpcIvWdfyQ6gNMSRpQ3h6Kck89ILCINW3R0NKNGjWLatGm+ROfzzz+ncePGDBkyBKvVSvfu3X37P/bYY0yfPp2vv/6a8ePHV+m1p02bRkFBAe+//z6hoUZi9vLLLzNmzBieeuop7HY7WVlZXHDBBbRubXSv6djx6Fwaqamp/O1vf6NDhw4AtG3btkrxiPiDj5alkpnnomXjUEZ1STQ7HBGpZX6T6ORaw8ANxc5DZoci0vDYQ4yWlWri8XjIzskhIjwcq/U03TnsFRu8O3bsWG6++WZeffVVHA4HH374IVdffTVWqxWn08kjjzzCzJkz2b9/P8XFxeTn55OamlqFd2PYsGED3bt39yU5AAMHDsTj8bBp0yYGDRrEuHHjGDlyJMOHD2fYsGFceeWVJCYaN2cTJkzgpptu4oMPPmDYsGFcccUVvoRIRI5XWOzmjR+3A3DbOa2wWdVqIuJv/KZDaL41DABvnhIdkWpnsRjdx6pzsYeUb78KdvkYM2YMXq+XmTNnsnv3bn788UfGjh0LwP3338/06dN58skn+fHHH1m9ejVdu3alqKh2qjW+8847LFmyhAEDBvDJJ5/Qrl07fvnlFwAeeeQR1q9fz/nnn8/8+fPp1KkT06dPr5W4ROqj6av2ciC7kISIIC7p2dTscETEBH6T6BTajETHkn/E5EhExExBQUFceumlfPjhh3z00Ue0b9+eXr16AbB48WLGjRvHJZdcQteuXUlISGDnzp3V8rodO3ZkzZo15Obm+rYtXrwYq9VK+/btfdt69uzJxIkT+fnnn+nSpQvTpk3zPdeuXTvuvfde5syZw6WXXso777xTLbGJNDRuj5epC7cBcPOgVgQG+M3tjogcw2/+5xcFGImOtSDT3EBExHRjx45l5syZvP32277WHDDGvXz55ZesXr2aNWvW8Kc//em4Cm1Vec2goCCuv/561q1bxw8//MBdd93Fn//8Z+Lj49mxYwcTJ05kyZIl7Nq1izlz5rBlyxY6duxIfn4+48ePZ8GCBezatYvFixezfPnyMmN4ROSoWWv3s/NQHtEhdq45I9nscETEJH4zRqe4JNEJLMo0NxARMd25555LTEwMmzZt4k9/+pNv+3PPPccNN9zAgAEDaNy4Mf/3f/9HdnZ2tbxmSEgI33//PXfffTd9+/YlJCSEyy67jOeee873/MaNG3nvvfc4dOgQiYmJ3Hnnndx6660UFxdz6NAhrrvuOg4cOEDjxo259NJLmTRpUrXEJtKQeL1eXl1gtOb8ZWBLQgL95lZHRP7Ab/73u+1GohNUnGVyJCJiNqvVyr59xxdPaNGiBfPnzy+z7c477yyzXpGubN4/zPHTtWvX485fKj4+/qRjbgIDA/noo4/K/boi/mzBpgw27M8mNNDG9f1bmB2OiJjIb7queUsSnRC3Eh0REZGG6pUftgJw7ZnNiQzRXFMi/sxvEh0CjUQnwpNDsbt6+tyLiP/68MMPCQsLO+HSuXNns8MT8UvLdhxmxa4jBAZYufGslmaHIyIm85uua1aHMXdFlMVJZr6LxmEOkyMSkfrswgsvpF+/fid8zm7Xt8giZihtzbm8d1PiIoJMjkZEzOY3iU5pMYIonOzMLVKiIyJVEh4eTnh4uNlhiEiJdXuzWLg5A6sFbhukyXRFxI+6rpWWlw6weMjJ1KShItXhj4PtpX7SdZSG4LWSSmtjuifRrFGIydGISF3gN4mOxxpIAUYrTm5WhsnRiNRvpV2z8vLyTI5EqkPpdVSXO6mvtmc4mbVuPwC3D1ZrjogY/KbrGkCuLYIgdwYFSnREqsRmsxEVFUV6ejpgzAFjsViq7fwej4eioiIKCgqwWv3m+5ha5/V6ycvLIz09naioKGw2m9khiVTKfxdux+uFYR3j6JAQYXY4IlJH+FWikx8QCe4MXM6DZociUu8lJCQA+JKd6uT1esnPzyc4OLhaEyg5saioKN/1FDGb1+tl8dZDOAtd5dq/sNjDl7/uAeCOIW1qMjQRqWf8KtEptEdCIRQ7NUZHpKosFguJiYnExcXhcpXvhqS8XC4XixYtYtCgQepOVcPsdrtacqROmbpwO0/N3ljh485sFUOvZtE1EJGI1Fd+legUO6LBCeQfNjsUkQbDZrNV+42yzWajuLiYoKAgJToifmTnwVymzN0MQNcmkTgCytd1Nchu48HRHWsyNBGph/wq0fEGG9/0WPIzzQ1EREREyvB6vfxjxloKiz0MaN2ID2/qp66rIlIl/jXKtyTRsRceMTkQEREROdaXq/ayeOshHAFWnrykq5IcEakyv0p0bKGNAHC4skyOREREREodchby+MzfAfjr0La0aBxqckQi0hD4VaITGN4YgOBiJToiIiJ1xRMzN3Akz0X7+HBuGdTK7HBEpIHwq0QnKMJIdEI92ZoJXEREpA74actBvvx1LxYLTL6sK3abX92aiEgNqtBfk8mTJ9O3b1/Cw8OJi4vj4osvZtOmTac97rPPPqNDhw4EBQXRtWtXZs2aVemAqyIkKhaAKHLId7lNiUFEREQM+UVuHpy+FoDrzmyu8tAiUq0qlOgsXLiQO++8k19++YWUlBRcLhcjRowgNzf3pMf8/PPPXHPNNdx44438+uuvXHzxxVx88cWsW7euysFXlCM8BoAonBzJq955P0RERKRiXpy/hdTDeSREBHH/yPZmhyMiDUyFykvPnj27zPq7775LXFwcK1euZNCgQSc85oUXXuC8887jb3/7GwCPPfYYKSkpvPzyy0ydOrWSYVeOJcQoRhBmKWBXtpMmUcG1+voiIiJi2LA/m9cXbQfg0Ys6Ex6kObNEpHpVaR6drCxjUH9MTMxJ91myZAkTJkwos23kyJHMmDHjpMcUFhZSWFjoW8/OzgaM2dIrMwN76TEuWwhWrNjwkHVoP67EqAqfS+oO33WtxL8Jqdt0besOXQOpCW6Pl79/uRa3x8t5nRMY0TnB7JBEpAGqdKLj8Xi45557GDhwIF26dDnpfmlpacTHx5fZFh8fT1pa2kmPmTx5MpMmTTpu+5w5cwgJCalsyKTMncdgQojEya+//EhG6o5Kn0vqjpSUFLNDkBqia2u+vLw8s0OQBuiDJTtZszuTcEcAj1zY2exwRKSBqnSic+edd7Ju3Tp++umn6owHgIkTJ5ZpBcrOziY5OZkRI0YQERFR4fO5XC5SUlIYPnw42euiiHQ5adU0lmHnja7OsKWWHXtd7XZ1eWhIdG3rjtIWdZHqsi8zn/98bxQyemBUBxIig0yOSEQaqkolOuPHj+fbb79l0aJFNG3a9JT7JiQkcODAgTLbDhw4QELCyZupHQ4HDofjuO12u71KNz12u51CexS49uDNO6IbqAaiqv8upO7StTWffv9SnbxeLw9/tY7cIje9m0cz9oxmZockIg1Yhaqueb1exo8fz/Tp05k/fz4tW7Y87TH9+/dn3rx5ZbalpKTQv3//ikVaTVyBUQC4cw+b8voiIiL+ava6NOZuSMduszD50q5YrRazQxKRBqxCLTp33nkn06ZN46uvviI8PNw3ziYyMpLgYKOC2XXXXUeTJk2YPHkyAHfffTfnnHMOzz77LOeffz4ff/wxK1as4PXXX6/mt1I+7iCjRr8l/4gpry8iIuKPsvJd/Ovr9QDcdk5r2sWHmxyRiDR0FWrRee2118jKymLw4MEkJib6lk8++cS3T2pqKvv37/etDxgwgGnTpvH666/TvXt3Pv/8c2bMmHHKAgY1KtioEGctVKIjIiJSW56evZH0nEJaNg7lziFtzA5HRPxAhVp0vF7vafdZsGDBcduuuOIKrrjiioq8VI2xhhqJTmBRprmBiIiI+IkVOw/z4dJUAJ68pCtBdpvJEYmIP6hQi05DYA9vDECQK9PcQERERPxAUbGHiV+uBeDKPk3p37qRyRGJiL/wu0THEW78gQ1xq2SqiIhITfts5W62pDtpFBrIg6M7mh2OiPgRv0t0giPjAAj35OD2nL4rnoiIiFROsdvD1IXbABh/bhuiQgJNjkhE/InfJTqh0bEARFlyyM53mRyNiIhIwzVz7X52H84nJjSQq/tqzhwRqV1+l+jYQ42ua1HkciS30ORoREREGiaPx8urPxitOTcMbEFwoAoQiEjt8rtEhxCj6prd4iYrS5OGioiI1IT5G9PZdCCHMEcAf+7fwuxwRMQP+V+iYw+mEAcAeZnpJgcjIiLS8Hi9Xl5ZsBWAa89sTmSw3eSIRMQf+V+iAzhtEQAUZB80ORIREZGG55fth/k1NZPAACs3nNXC7HBExE/5ZaKTH2AkOq4cJToiIiLV7dWS1pwr+zQlLjzI5GhExF/5ZaJTFBgFQLHzkLmBiIiINDBr92Tx45aD2KwWbh3U2uxwRMSP+WWiUxwYDYA374jJkYiIiDQspa05F3ZPIjkmxORoRMSf+WWi4wk2Eh1rgRIdERGR6rI13cns9WkA3D5YrTkiYi6/THQsJSWmAwqV6IiIiFSXqQu34fXC8E7xtIsPNzscEfFzfpno2MKMSUMDi7JMjkRERKRh2JuZz4xf9wJwh1pzRKQO8MtEJzC8MQDBxUp0REREqsMbi7ZT7PEyoHUjejaLNjscERH/THSCI2IBCPNkmxyJiIhI/XfQWchHy1IBuHNIG5OjEREx+GWiExJlJDqR3hwKXG6ToxEREanf3lm8g8JiD92bRjKgdSOzwxERAfw10Yk0Ep0oi5MjeUUmRyMiIlJ/ZRe4eH/JLgBuH9wGi8VickQiIga/THRKq66FW/I5kp1rcjQiIiL11/9+2UVOQTFt4sIY0Sne7HBERHz8MtEhKBIPxjdOuVkZJgcjIiJSPxW43Lz90w4Abj+nNVarWnNEpO7wz0THaiPXEgZAvhIdERGRSvl0xW4OOotoEhXMhT2SzA5HRKQM/0x0gLyACAAKs5XoiIg0JG63m4ceeoiWLVsSHBxM69ateeyxx/B6vb59vF4vDz/8MImJiQQHBzNs2DC2bNliYtT1j8vt4b8LtwNw6zmtsNv89pZCROoov/2rVBAQBYDLecjcQEREpFo99dRTvPbaa7z88sts2LCBp556iqeffpqXXnrJt8/TTz/Niy++yNSpU1m6dCmhoaGMHDmSgoICEyOvX75evY+9mfk0Dgvkyj7JZocjInKcALMDMIsrMAryweM8bHYoIiJSjX7++Wcuuugizj//fABatGjBRx99xLJlywCjNWfKlCn885//5KKLLgLg/fffJz4+nhkzZnD11VebFnt94fF4eW3hNgBuOKslQXabyRGJiBzPbxMdd1A0ZAH5R8wORUREqtGAAQN4/fXX2bx5M+3atWPNmjX89NNPPPfccwDs2LGDtLQ0hg0b5jsmMjKSfv36sWTJkhMmOoWFhRQWFvrWs7ONCaddLhcul6vCMZYeU5lj64I5vx9ga7qT8KAAru6dVG/fR02o79dWTk7Xtu4o7zXw20THGxwNgK1AiY6ISEPy97//nezsbDp06IDNZsPtdvPEE08wduxYANLS0gCIjy9bCjk+Pt733B9NnjyZSZMmHbd9zpw5hISEVDrWlJSUSh9rlgI3vLzeBlg4s1ERP86vf++hNtTHayvlc6pra/MU4nBl4yjOJrA4B6u3GIvXgwUPFq/7mMclC27fY7DgCgihyBZGUUAYrpKfRQGheC1+e8t+Qnl5eeXaz29/a9ZQY+Zme5ESHRGRhuTTTz/lww8/ZNq0aXTu3JnVq1dzzz33kJSUxPXXX1+pc06cOJEJEyb41rOzs0lOTmbEiBFERERU+Hwul4uUlBSGDx+O3W6vVEy1rdDl5sNlu5m6aAdH8lwE2a08du05NApzmB1anVIfr61f8LghKxXLwc1YDm7CcnALHNoKnmIIDD262EPxllkPK1kPo9hqZ+3yn+jeOglbwWEsuRmQmwF5B0seH8Tiqpn5Gb2OcAiOMb6oD46B4Gi8Ma3w9LsDHOE18prVzuuBzFQs6b9jydiANywBb4+xlTpVaav66fhtohMQZiQ6QcVZJkciIiLV6W9/+xt///vffV3Qunbtyq5du5g8eTLXX389CQkJABw4cIDExETfcQcOHKBHjx4nPKfD4cDhOP6G3m63V+lmtqrH14Zit4fPV+7hhXlb2J9lFGto1TiUSRd1JiE6zOTo6q76cG0bpOIiOLwNMjbBwc2QsREyNsOhLVBctWIjAUBfgJ2n2dHmgLA4CIkxHlsDwGozFovt5OteLxRkQt5hyD9s/CzIBMBSmAOFOVgyd5V9qd9nwFUfQFzHKr23MjweWPeF8XsMi4Ow+JIlDkLjwB50+nPkHoQD6yF9A6SvhwO/G9eiyHl0n2YDoO+4SoVY3v9bfpvoOCJiAQgpLl9GKCIi9UNeXh5Wa9miojabDY/HA0DLli1JSEhg3rx5vsQmOzubpUuXcvvtt9d2uHWWx+Nl1rr9PDdnM9sPGt9SJ0YGcffQtlzeuykBKictdcWhbbB+Omz4GtLWgdd94v1sDmjcFmLbQ+P2ENsOAoKNm++i3GMW5zHbjj7nLczlUJ6LmKbtsYaX3PSHNi5JAGKPLo5wsFTT5LkeN+RnHk18Sn/mHYSlrxsJ3BvnwoUvQdfLq/56mbvhqztgx6KT7xMUeUzyU7rEgjPjaFKTm37iY22Bxu8/rjM07VP1eE/DbxOdkMjGAIR5svF4vJrNWUSkgRgzZgxPPPEEzZo1o3Pnzvz6668899xz3HDDDQBYLBbuueceHn/8cdq2bUvLli156KGHSEpK4uKLLzY3+DrA6/WycHMG//l+E+v3GV8GRofYuXNIG649s7kqrEndcHALrJ8Bv8+AA+vKPhcYbiQxsR2gcTvjxjq2PUQ1N1pOKqnY5WLxrFmMHj0aa2211lltENrIWP6ox1j44kbYvsD4uXspjHgCAgIr/jpeL/z2Ccz6GxRmgz0EOl1stCg5D4Az3fjpLoKCLGM5uPnU54xuYSQ08Z0grhPEd4aY1mCrvfTDbxOdsOg4AKItTnIKiokMUfOyiEhD8NJLL/HQQw9xxx13kJ6eTlJSErfeeisPP/ywb58HHniA3NxcbrnlFjIzMznrrLOYPXs2QUHl6JLRgK3cdZinZm9i2Q5j6oUwRwA3nd2SG89qSXiQPifFZBmbjiY36b8f3W4NgJbnQKeLoM1QiGhSfS0qdVloY7j2S/jhSfjxGVj2OuxbDVe8C5FNyn+e3IPw7T2w4RtjvWlfuOS/0Kh12f1Ku9blHCib/JQ+Do46mtDEdgCH+V1b/TbRsYcZLTpRONmbW6hER0SkgQgPD2fKlClMmTLlpPtYLBYeffRRHn300doLrA7bmu5k8qwNzNtodDcJDLBy3ZnNuWNIG2JCK/HtsEgpjxuy90HmLjiyy5jWwx5ktBj4lmAIDDl+mz0EDh6T3GRsPHpeawC0GgKdL4b2o43xMP7IaoOhDxnJyfRbYM8y+O8guPxtaHXO6Y/fOAu++atRVMFqhyETYcDdJ251sVggONpY4jpU/3upAX6b6FBSXtpucZOVdRhizc86RUREapvb4+W6t5ayL6sAm9XCFb2b8tehbUmKCjY7NKkPvF7j2/zSRCZzJ2SmljzeBVl7jMpm1cFqh9bnliQ3o3z3cgK0Pw9uWQif/hnS1sIHF8O5D8HAe8B6gvF0Bdnw/UT49X/GelwnoxUnsVttRl3j/DfRCQyhkEAcFJGXmQE0MzsiERGRWrfnSB77sgoIDLDy3d1n01pf/NVd2fthxm1GeeE+f4EWZ1dfFy1nBqyZBptmgyvPaInxuo2fnuJjHpduLzYeu/LBXXjqc1vtEJVsjJEJjTWqn7nyjGNdeVB0zOPSpZQtEFoPNZKbducZ3aPkxGJawo0pMPN+WP0/mDcJ9iyHi18r+3vb+RNMvx2yUgELDLgLhvyjfNXU6hn/TXSAXFsEDvdB8rMzzA5FRETEFNsyjHKvrRqHKsmpy1z58PGfYN8qY339l9CoDfT+C/T4U+W6bnk8sHMRrHwXNnwLnvLNNn88izEuJrq5kcz88Wd44olbFU7G6y1JfPKPdmuT8rEHw0UvQ/IZRmGBTbPg9cFGCepGbWH+Y7DkFcBrXJtLpkLzAWZHXWP8OtHJs0UQ4z6IK+eQ2aGIiIiYYmu6kei0jlOSU2d5vfD1XUaSExwNHS805jk5tBXm/APmPWq0ePS5AZL7nb6VJ+cArP4QVr0HR3Ye3d6kt1HJKzLZSEysAWXnebHYjs7/UvpcgMNIZCpT6etkLBYjuVGCUzkWC/S+3uiG9ul1cGQHvDnMSEYPbzP26XU9jHyi/kw2Wkl+negUBUZBERQ7leiIiIh/2pZuzJHTRq05dddPz8Paz4zk4sr3oeUg4yZ17eew4m1I+80oDfzbJ8ZYi95/ge5XGfOdlPJ4YPsPRuvNpllHx804IqDblcaNbwMbn+H3knoa43a+vAW2phhJTmicMedO+/PMjq5W+HWiU+yIAid48w6bHYqIiIgptmaoRadO2/Sd0WIDMOopI8kB45v4Pn+B3uNg7ypY+Tas/cIou/zd32Duv6DLpdDtKti9zGi9yUw9et6mZxjHdr4YAkNr+U1JrQmJgT99CktfM67/oAdOPCdPA+XXiY4nyOjPaik4YnIkIiIitc/r9fq6rqlFpw5K3wBf3AR4oc+N0Pem4/exWKBpb2MZ8YTRqrPiHcjYYFTUKq2qBUYLT7erjW5N8Z1r7W2IyaxW6H+n2VGYwq8THUvJwL2AQiU6IiLifw7lFpGV78JigVax+la/Tsk7DB9dDUVOo7raqKdOf0xwFPS7Fc64BVJ/gZXvGFXU4joarTedLtK4F/Erfp3o2MKMprvAokxzAxERETHBtpLWnKbRwQTZbSZHIz5uV8kg8p1GZawr3gNbBSY2t1igeX9jEfFjFaj11/DYwxsDEFycbXIkIiIitc83Pkfd1uqW7/4Pdv4IgWHwp0/8akyFSHXy60QnqCTRCXVnmRyJiIhI7VPFtTpo+Zuw4i3AApe9aXQ7E5FK8etEJzQqDoBIbw6FxW6ToxEREaldqrhWx+xYBLMeMB4PfRjajzI3HpF6zq8TnZCoWACiLE6y8io7G7CIiEj9VDpGp40SHfMd3m6My/G6oesVcNa9ZkckUu/5daJjLenzGmHJ50hOnsnRiIiI1J68omL2ZuYDGqNjuoJs+OgayD8CSb2MCR0tFrOjEqn3/LrqGkGReLBgxYszMx2axJgdkYiISK3YnmGMz4kJDSQmNNDkaBoAjxv2r8a6dQGd9i7H+ssOiEyCsDgIizeW4OjjExiPG768GTI2QlgCXP0h2IPNeQ8iDYx/JzpWG7mWUMK9TvKyMoAOZkckIiJSK7b5Kq5p/pxK8Xrh0FbYvsBYdv4IBVnYgLYA8747/hirvWziExYH+Ydh82ywOeDqaRCRVKtvQ6Qh8+9EB8izRRJe7KQw+6DZoYiIiNQajc+phOz9sGNhSXKzEHL2lX3eEYGn+UC2H/HQKj4ca24GOA8YS/4R8Lgge6+x/NFFr0DT3rXyNkT8hd8nOgX2SCjeS5HzkNmhiIiI1Jo6O4eOMx3SN0BkU4hpVT1jVbxeo2vYjh+NRCVjI9gCIcABAcFgD4KAksUefHR7gMNYd6Ybxx3cXPa8tkBI7getBhtLYg/cHi/rZ82i+ejRWO3HTPJZXAi5GZBz4Gjy40w3fjbpDd2uqPr7FJEy/D7RKQqMgnzw5B42OxQREZFaUzqHjqmlpQuyYf9q2LsK9q6Efb9C1u6jzwdFQZNexgD9Jr2Nx+EJpz+v12tUMduxyFh2/gS56dUQsAWSekDLc4zEptmZx4+n8ZykimuAw0jeIptWQxwiUh5+n+i4g6IhC8hToiMiIv6h2O1hx8Faniy0uBAOrCtJakoSm4ObAe8fdrRAdAvI3gcFmbBtvrGUimgCST2PJj5JPSEoEjJ3G+NkSpObP3YPCwiGZv2g5SDjWABXARSXLK58I8bi/JLtJeuufCOZaT4QWpwFISpcJFJf+H2i4w2OBsBWcMTkSERERGrHniP5FLk9OAKsNImqoQpfXq/RBW3TTNj8PexfA+6i4/eLbAZNShKXpF5Gi4kjHIqLIP33kpaekuQoY+PRMS4bvz16jtBYo1vYsax2SD7DSGxanA1N+xitKiLiN/w+0bGGGHPp2IsyzQ1ERESklmwtKUTQKjYMq7Ua52vxuGH3Utg401iO7Cj7fHDM0ZaY0sQmLPbE5woINJKepB7Ajca2QqeRMO0raRHauxIyU40kx2IzztvibCO5Se4HgSHV995EpN7x+0QnIMxIdByuTHMDERERqSWlpaWrpeJaUR5s/wE2zoLN30HeMcV9bA5jLEuH0ca4lugWVSsu4AiDFgONpVTuQWM8TmwHCIqo/LlFpMHx+0THEdEYgJDibJMjERERqR1VnkMn7zBs+s5otdk23xjPUiooCtqdZyQ3rYcayUlNCm1sLCIif+D3iU5IpNFkHu7Jxuv1YqmOMpYiIiJ12NaqzKGzeQ58fgMU5RzdFtkMOpxvJDfN+oPNfvLjRURqid8nOqFRcQBEWZzkFBYTEaQ/ziIi0nB5vV62ZZSUlq5oxbWl/4XZfwevBxq3hy6XGglOfJfqme9GRKQa+X2iU9p1LYoc0pxFSnRERKRBO+gsIivfhcUCLRuXs+uauxi+nwjLXjfWe/4Zzn/OKBggIlJH+X2iQ7BRDz/Q4iYr6wiU94++iIhIPVQ6Pic5OoQgu+30BxRkG13VtqYY68MmwcC71YIjInWeEh17MEXYCcRFblYGoBmLRUSk4Sodn1OuQgSZqTDtKmM+m4BguPR16HRhDUcoIlI9lOhYLDitEcR4DlGQnXH6/UVEROqxcpeW3rMCProGctMhLAGu+ciYp0ZEpJ5QogPkB0RC0SEKsw+aHYqIiEiNOtqic4pEZ/10mH4bFBdAfFf408cQqR4PIlK/KNEBCu2RUATFzsNmhyIiIlKjtpdUXDthi47XCz8+C/MfM9bbjoTL3wJHeC1GKCJSPawVPWDRokWMGTOGpKQkLBYLM2bMOOX+CxYswGKxHLekpaVVNuZqV+yINh4cO5uziIhIA5NbWMzeTGNyz+NadIoLYcYdR5OcM+8wuqspyRGReqrCLTq5ubl0796dG264gUsvvbTcx23atImIiAjfelxcXEVfusa4g4xEx5J/xORIREREas6Og0ZrTqPQQKJDjykNnXcYPrkWdi0Giw1GPw19bzIpShGR6lHhRGfUqFGMGjWqwi8UFxdHVFRUhY+rDZYQo8S0rVCJjoiINFwnHJ9TmAPvjIKMjeCIgCvegTbDTIpQRKT6VLjrWmX16NGDxMREhg8fzuLFi2vrZcvFFloyl05RprmBiIiI1KDSimutS8fneL3wzT1GkhOeCDd8ryRHRBqMGi9GkJiYyNSpU+nTpw+FhYW8+eabDB48mKVLl9Kr14nLVBYWFlJYWOhbz87OBsDlcuFyuSocQ+kxJzu2NNEJLs6q1PnFHKe7rlJ/6drWHboGDctxc+iseg/WfW50V7viPYjvZGJ0IiLVq8YTnfbt29O+fXvf+oABA9i2bRvPP/88H3zwwQmPmTx5MpMmTTpu+5w5cwgJCal0LCkpKSfcHpSeRisgpDiLWbNmVfr8Yo6TXVep/3RtzZeXl2d2CFKNysyhk7YOvvs/44lh/4Jm/UyMTESk+plSXvqMM87gp59+OunzEydOZMKECb717OxskpOTGTFiRJmCBuXlcrlISUlh+PDh2O32457P2RoJnzxPJE6GjzwPu63WevRJFZzuukr9pWtbd5S2qEv9V+z2+IoRtIkEPrvemCen7Qjof5e5wYmI1ABTEp3Vq1eTmJh40ucdDgcOh+O47Xa7vUo3PSc7PrJREgDRFie5LogN0o1VfVLVfxdSd+namk+//4Zj95F8XG4vQXYLTX56EA5thYgmcPFUsOoLPhFpeCqc6DidTrZu3epb37FjB6tXryYmJoZmzZoxceJE9u7dy/vvvw/AlClTaNmyJZ07d6agoIA333yT+fPnM2fOnOp7F1VkC20EQIQljy3OPGLDj0+yRERE6rPS8Tm3h/+MZd1nxricy9+Gks9AEZGGpsKJzooVKxgyZIhvvbSL2fXXX8+7777L/v37SU1N9T1fVFTEfffdx969ewkJCaFbt27MnTu3zDlMFxzle5hzJAMSo82LRUREpAZsy3DS3pLK7fn/NTYMfRianWluUCIiNajCic7gwYPxer0nff7dd98ts/7AAw/wwAMPVDiwWmW14bSEEeZ1kpeVAbQzOyIREZFqlbo/nVftLxDoLYI2w2HAX80OSUSkRqlTbolcm1HkoDD7oMmRiIiIVDOvlxE7nqK1dT/5QfFwyX81LkdEGjz9lStREBAJQJHzkMmRiIiIVC/vrx8wuPAHir1W0ke+qnE5IuIXlOiUKAqMAsCTq0RHREQakAPrYdbfAHjOfSXxXerQGFkRkRqkRKeEOyjKeJB32NQ4REREqk2hEz4bh6W4gAXu7syKuJIgu83sqEREaoUSnRLe4BgArAVHTI5ERESkGni9MPM+OLiZXEccE1y30zqu4pNui4jUV0p0SlhDjETHXphpbiAiIiLVYfWH8NvHYLHxSfNJHCaC1nFhZkclIlJrlOiUCAhrDIDDlWluICIiIlWVvgFm3m88Pvcf/JDfGoA2sUp0RMR/KNEpYQ83KtCEuLNNjkRERKSKvv4rFOdD66Ew8F62pjsBaB0XanJgIiK1R4lOiZCoOADCPNmnnBBVRESkTstJgz3LAAtc9ApOl4f9WQUAtFaLjoj4ESU6JUKjYgGIIofcIrfJ0YiIiFTStvnGz6QeEJHI9gyjNadxWCBRIYHmxSUiUsuU6JQIijDG6EThJDO30ORoREREKmnrPONnm2EAbCtJdNSaIyL+RolOCUuIMUbHYSkmOzvL5GhEREQqweM+2qLTeigA29JzjVVVXBMRP6NEp5Q9hCLsADgzM0wORkREpBL2rYb8w+CIhKZ9AXyFCFRxTUT8jRKdUhYLTms4AHlZSnRERKQe2lbSba3VILAFGJtKu66pRUdE/IwSnWPkB0QC4Mo5aHIkIiIilbB1rvGzZHxOsdvDzkNG17U2SnRExM8o0TlGob000TlkciQiIiIVlH8E9iw3HpeMz0k9nIfL7SXYbiMxIsjE4EREap8SnWO4HFEAePOU6IiISD2zfSF4PdC4PUQlA5SZKNRqtZgZnYhIrVOicwxPUDQAlvwjJkciIiJSQX/otgawLaOk4poKEYiIH1Kic6zgGABshUp0RESkHvF6j5aVbnOub7MqromIP1OicwxbqDFpaGBRprmBiIiIVETGRsjeCwFB0Hygb7MqromIP1Oicwx7uDFpaHCxJgwVEZF6pLTbWouzwB4MgNfrZVtpi44SHRHxQ0p0juGIiAUg1J1tciQiIiIVsLVk/pySamsAGTmF5BQWY7VA80YhJgUmImIeJTrHCI0yuq6Fe3IodntMjkZERKQcivJg18/G42MKEZSOz2neKBRHgM2MyERETKVE5xihUXEARFtyyMp3mRyNiIhIOexaDO5CiEyGxm19m33jc2JDzYpMRMRUSnSOERBmtOhEWvI44sw3ORoREZFy8JWVHgqWo3PlHJ1DR+NzRMQ/KdE5VlCU76Ez86B5cYiIiJRXaaJzzPgc0Bw6IiJKdI5lC8BpMZr4czMzTA5GREQqa+/evVx77bU0atSI4OBgunbtyooVK3zPe71eHn74YRITEwkODmbYsGFs2bLFxIgr6chOOLQVLDZodU6Zp7aq4pqI+DklOn+QZ4sAoDBbiY6ISH105MgRBg4ciN1u57vvvuP333/n2WefJTo62rfP008/zYsvvsjUqVNZunQpoaGhjBw5koKCAhMjr4TSamvJ/SAo0rfZWVhMWrbxXtSiIyL+KsDsAOqa/IBIKN6Py3nI7FBERKQSnnrqKZKTk3nnnXd821q2bOl77PV6mTJlCv/85z+56KKLAHj//feJj49nxowZXH311bUec6Vtm2/8bHNu2c0lrTmx4Q4ig+21HZWISJ2gROcPigKjoADcSnREROqlr7/+mpEjR3LFFVewcOFCmjRpwh133MHNN98MwI4dO0hLS2PYsKOlmCMjI+nXrx9Lliw5YaJTWFhIYWGhbz0725hvzeVy4XJVvEpn6TGVOdbHXUTA9gVYAFeLwXDMuTanGRNft2ocUrXXkAqrlmsrdZKubd1R3mugROcP3A6ja4MnT4mOiEh9tH37dl577TUmTJjAgw8+yPLly/nrX/9KYGAg119/PWlpaQDEx8eXOS4+Pt733B9NnjyZSZMmHbd9zpw5hIRUfjLOlJSUSh/bKGcjZxU5KQwIZ/aqPWDZ53vu+1QrYCUg7xCzZs2q9GtI5VXl2krdpmtrvry8vHLtp0TnD4IjG0MG7N23j32Z+SRFBZsdkoiIVIDH46FPnz48+eSTAPTs2ZN169YxdepUrr/++kqdc+LEiUyYMMG3np2dTXJyMiNGjCAiIqLC53O5XKSkpDB8+HDs9sp1LbP+sAq2gr3DSEaff0GZ576dthr2pjOkd0dG929eqfNL5VTHtZW6Sde27ihtVT8dJTp/0KxpU9gK4Z4sHv3md6b+ubfZIYmISAUkJibSqVOnMts6duzIF198AUBCQgIABw4cIDEx0bfPgQMH6NGjxwnP6XA4cDgcx2232+1VuuGp0vHbjfE51rYjsB5zDq/Xy4a0HADaJUTqhswkVf23IXWXrq35yvv7V9W1P7DEtAKgm3Uns9en8cOmdJMjEhGRihg4cCCbNm0qs23z5s00b260bLRs2ZKEhATmzZvnez47O5ulS5fSv3//Wo210pzpkPab8bh12UIEK3cdYc+RfEICbfRqHn2Cg0VE/IMSnT9qNRiALtYdNCKLf321ngKX29yYRESk3O69915++eUXnnzySbZu3cq0adN4/fXXufPOOwGwWCzcc889PP7443z99desXbuW6667jqSkJC6++GJzgy+v0mprid0hLLbMU5+t2APA6K6JhDnUcUNE/JcSnT8KizM+OIALQjeQejiP1xZsMzkoEREpr759+zJ9+nQ++ugjunTpwmOPPcaUKVMYO3asb58HHniAu+66i1tuuYW+ffvidDqZPXs2QUFBJkZeAVvnGj9bDy2zOa+omG9/M4oSXNG7aW1HJSJSpyjROZGSD45bknYA8NrCbew8mGtmRCIiUgEXXHABa9eupaCggA0bNvhKS5eyWCw8+uijpKWlUVBQwNy5c2nXrp1J0VaQx3PM/DnDyjz13do0covcNG8UwhktY0wITkSk7lCicyIlHxxJh5YwqE0MRcUe/vX1erxer8mBiYiI39u/GvIOQWA4JJ9R5qnPVu4G4PJeTbFYLCYEJyJSdyjROZHkMyAwHEveQZ7s7yXQZmXh5gy+X3/i+RVERERqzbaSIgqtzgHb0cpDqYfy+GX7YSwWuEzd1kRElOickM1ufIAATQ8t5rZzjEpsk775ndzCYjMjExERf7e1JNFpU3Z8zuclrTlntWmsOeBERFCic3KlHyBb53HHkDYkxwSzP6uAF+dvMTcuERHxXwVZsHuZ8fiYQgQej5cvVu0F4Io+yWZEJiJS5yjROZnSD5DdywhyO3lkTGcA3vpxB5sP5JgYmIiI+K3tC8HrhkZtIbq5b/PP2w6xNzOfiKAARnSKNzFAEZG6Q4nOyUQ3Nz5IvG7YvpChHeMZ3imeYo+Xf85Yp8IEIiJS+0rLSv+h2lppEYILeyQRZLfVdlQiInWSEp1TKf0gKflg+deYTgTZrSzbcZgZq/eaGJiIiPgdr/eYstJHu61l5buYvc4olnNFb3VbExEppUTnVEoTnW3zweulaXQId53bFoAnZm4gK99lYnAiIuJXDm6GrN1gc0Dzgb7N36zZR2Gxh3bxYXRrGmligCIidYsSnVNpPsD4QMnabXzAADef3YrWsaEcdBbx3JxNJgcoIiJ+o7TbWvMBEBji2/zZyj2A0ZqjuXNERI5SonMqgSHQouRbs5IPmMAAK49d1AWAD37Zxdo9WWZFJyIi/sRXVvro+JwtB3JYszuTAKuFi3s2MSkwEZG6SYnO6fjG6czzbRrQpjEXdk/C44V/frUOj0eFCUREpAa58mHXYuPxMYlOaWvOkA5xxIY7zIhMRKTOUqJzOqVlpnctNj5oSvzz/I6EOQJYszuTj5fvNik4ERHxC7sWQ3EBRDSB2PYAuNweviydO6d3UzOjExGpk5TonE5se4hoanzA7Fzs2xwXEcSE4e0AeGr2Rg45C82KUEREGjpft7WhUDIOZ8GmDA46C2kcFsiQDnEmBiciUjcp0Tkdi+VoGc9t88o8dV3/5nRMjCAr36VWHRERqTlpa42fzQb4Nn22wvjcubhHE+w2fZyLiPyR/jKWR2miU1rxpkSAzcrFPZIA2JiWU9tRiYiIv8jPNH6GxQJw0FnI/I3pAFzRR3PniIiciBKd8mh5DlhsRonpzNQyT7WJCwNga7rTjMhERMQf5B8xfgZHAzDj170Ue7x0axpJ+4RwEwMTEam7lOiUR3AUJJ9hPN5atvta61gj0dme4VT1NRERqRm+RCcGr9fL56Vz56g1R0TkpJTolFfrE3dfS44JITDASmGxh72Z+Sc4UEREpAqKC8GVazwOjmbd3mw2puUQGGDlwm5J5sYmIlKHKdEpr9JxOtsXgtvl22yzWmjVOBRQ9zUREakBpa05Fis4Ivi0pAjByM4JRIbYTQxMRKRuU6JTXok9IKQRFOXAnuVlnirtvrYtQ4mOiIhUs9JEJyiKAreXr1Zr7hwRkfJQolNeVutJu6+1VkECERGpKccUIkj5/QDZBcUkRgYxsE1jc+MSEanjlOhUxEnKTLeOVdc1ERGpIcckOp+VFCG4rFdTbFaLiUGJiNR9SnQqovW5xs/9a8CZ4dtcWmJaXddERKTalSQ6BfYIftxifPZcrm5rIiKnpUSnIsLiILG78XjbfN/mVo3DsFjgSJ6LQ85Ck4ITEZEGqSTR2ZXnwOuFM1rG0KKkCI6IiJycEp2KOsE4neBAG02igo3N6r4mIiLVqSTRWXvYBqgIgYhIeSnRqag2w4yf2+aDx3N0s6/7Wq4ZUYlIXZG2FmbeX6Z7q0iVlCQ6u/MdhATaGN010eSARETqByU6FZV8BgSGQ95BSFvj29wmVpXXRAT4+q+w/A2Y/XezI5GGIu8wAJmEcX7XREIdASYHJCJSPyjRqSibHVqdYzw+pvuar8S0ChKI+K+9K2HfKuPxus9h/2/mxiMNQ0mLTqY3lHPax5ocjIhI/VHhRGfRokWMGTOGpKQkLBYLM2bMOO0xCxYsoFevXjgcDtq0acO7775biVDrEF+Z6XlHN5V2XVOLjoj/Wv6W8dNijKVg3iTzYpGGoyTRySKMmNBAk4MREak/Kpzo5Obm0r17d1555ZVy7b9jxw7OP/98hgwZwurVq7nnnnu46aab+P777yscbJ1RWpBg9zIoyAKOdl3bm5lPXlGxWZHJqaT+Aqs+KDO2SqTa5B2GdV8Yjy+ZCtYAo9V3x4/mxiX1X34mAJleJToiIhVR4Y6+o0aNYtSoUeXef+rUqbRs2ZJnn30WgI4dO/LTTz/x/PPPM3LkyIq+fN0Q3RwatYVDW2D7Quh0IdGhgcSEBnI4t4jtGbl0aRJpdpRSyuuFn1+ClIcBL3jd0Huc2VFJQ/PrB1BcAAndoOsVxhchy9+Auf+Cm+aBRZM7SuV4849gATIJJTpEiY6ISHnV+IjGJUuWMGzYsDLbRo4cyT333HPSYwoLCyksPDofTXZ2NgAulwuXy1XhGEqPqcyxJ2NtdS62Q1vwbJ6Du62R+LVqHMLh3CI27c+ifVxItb2WnFi5rmtxIbbv7sf620e+Td4fnqS4w8UQqHko6qqa+D9bozxuApa/hQUo7n0D3uJiGHAvAaunYdm7kuJ1X+HtcL7ZUVZKvbkGDZXbhaUoBzBadKJC7CYHJCJSf9R4opOWlkZ8fHyZbfHx8WRnZ5Ofn09wcPBxx0yePJlJk47v2z5nzhxCQiqfQKSkpFT62D+Kyw6nP1CwfhYpDAOLhcB8K2Dl+yVrCNj7a7W9lpzaya5roCubM3a8QKPcLXixsL7J1bTMmEuo8wBb/3cfmxMurOVIpaKq8/9sTYrLWkP/zF0U2UKYsycU975ZAHSIGUr7A1+T/+3f+WGbB2/p2J16JC8vz+wQ/FtJtzWP14I7MAJHQP37NyQiYpY6WaNy4sSJTJgwwbeenZ1NcnIyI0aMICIiosLnc7lcpKSkMHz4cOz2avo2zDUY77MvEeI6xOh+baFxOw78vIufv9uEJTKB0aN7VM/ryEmd8roeWE/AZ9diyd2N1xGB+9K3aN9qCJZ1n8NXt9Hh0GzaXP0EhDQyJ3g5pRr5P1uDbB+/b/zsfR0jh19y9InCs/G+8hPh+fsZ3SQLb49rTYqw8kpb1MUkJYUIsgkhMjTI5GBEROqXGk90EhISOHDgQJltBw4cICIi4oStOQAOhwOHw3HcdrvdXqWbnqoeX/ZkkdBiIGybj33nAkjsTLsEIwnbcSivXtycNRTHXdeNM+GLm8GVCzGtsFzzCQGx7Yznul8FS1/FkvYb9p+nwKh/mxKzlE+1/p+tKYd3wDajAqOt3y3Yjo3XHgOD7ofvHyRg0dPQ42qwn/jvXl1V53//DZ2vtHSYxueIiFRQjc+j079/f+bNm1dmW0pKCv3796/pl655bUrGHpWUmS4tMb3jYC7FblX2qnVeL/z4HHw81khyWg4yBoGXJjkAVisML+kWufxN4yZVpCpWvAV4jWqMjVof/3yfGyGiKeTsg2Vv1Hp4Us/ll04WGkq0Kq6JiFRIhRMdp9PJ6tWrWb16NWCUj169ejWpqamA0e3suuuu8+1/2223sX37dh544AE2btzIq6++yqeffsq9995bPe/ATKVlpnctBlc+SZHBBNttuNxeUg+rX3utchXA9NtK5i3xGjeX134JITHH79v6XGg1BDwu+OGJWg9VGhBXPvz6P+PxGTefeB97EAx50Hj847O+MRci5VI6h443jGgVIhARqZAKJzorVqygZ8+e9OzZE4AJEybQs2dPHn74YQD279/vS3oAWrZsycyZM0lJSaF79+48++yzvPnmm/W3tPSxYtsb39QWF8DOxVitFlrFGpW8tmXkmhycH3Gmw3sXwG8fGxM1jn4GLngObKe4KSht1Vn7GexbXSthSgO07kvjRjSyGbQdcfL9ul8NsR2gIBMWv1Br4UkDUNp1DXVdExGpqAonOoMHD8br9R63vPvuuwC8++67LFiw4Lhjfv31VwoLC9m2bRvjxo2rhtDrAIsF2g43Hi/8N7iLaV0ycejWdKeJgfmPiLxdBLwzHPYsh6BIuPaLk3+zfqzE7sZcJ2DMc1IdsvcZk0aK/1he0hWtz1/AeopqWFYbDDW+DOKX1yAnreZjk4bBN0ZHc+iIiFRUjY/RafDOvg8ckcaN9k/P+cbpbMtQolOtXAVwaJsxQeuvH8KCp7B9ezdnb3kMS/ZeaNQGbpoPrYeU/5zn/hNsgbB9AWybX7X4ts2HF3rAC91h69yqnUvqhz0rYd+vxr+hXtedfv/2oyG5HxTnw8Knaj4+aRiObdEJVdc1EZGKqJPlpeuVqGQ4/1n48iZY8G96D+4OqEWnUgqdRsKQmQpZeyBrt/Ezey/kZhy3u7Vk8bQcjPXKdyE4umKvF90C+t4Ev7wKKf+CloONYgUVlbrUKIDgLjSWD6+A856CfrdU/FxSf5S25nS+FEIbn35/iwWGPQLvjIKV70H/8ScuXiByrGPG6LRSi46ISIUo0akO3a6AzbNh3ef0XfV/BPMQ29KdeL1eLBaL2dHVfc4MWPZfoyJVQebJ97OHQGRTiGgCkU1xhyexKtVJj6sfwuqoZMnes+83BpOn/QbrPoduV1bs+P2/GYmNK88oThGeAKs/hO/+Bgc3wXn/PvVYofIqyIYtcyCmJST1Mm6axTy5h4zxOVC+rpKlmg8wxvJsmQPzH4Mr3q2R8KQBKUl0jqi8tIhIhSnRqS7nPwOpSwjM2sFD9g95sPBGMnIKiYvQBG8ndXg7/PyykRgUFxjboltAk94lCU1T42fpEhxd5gbf43Kxb9Yselir8M84tBEMvNu46Zz/GHS6CAKOn8PphA5ugQ8ugcIsaNYfrvqfMUdKbHujhWj5m3Boq3EzW9HWplJeL6z7Ar7/BzhLxnXEd4Xe1xtjjIKjKndeqZpfPzBa7xK7G/9eK2Lov2BLCqyfbvzbS+pZMzFKw6CuayIilaYxOtUlOBoumQpY+JNtHudaV6n72snsXQWfXg8v9TbmICkuMFoprnwf7loFl78Nwx81un51GA2J3Ywy0TXVinHmHRCWYHSZW/5W+Y7JTIX3L4K8g8bN7p8+gcAQI8aBd8PVH4I91Bj/8+YwY3xRRaVvgPfGwBc3GklORBOwOeDAWph1PzzbwSipvWuJkRBJ7fC4S+bOAfreXPF/lwldjrYczp1UvbFJg+MtKXCSpWIEIiIVpkSnOrUcBP3vBOBp++vs2bPL5IDqEK/XGKT/3hh4Ywj8PgO8HmgzHMbNhJvnG60pp6pcVVMCQ2DIROPxov9AQdap9885YCQ52XuhcTtjvp6gyLL7dDgfbvzeaJU6tBXeOBd2LCpfPIU5MOefMPUs2PkjBATBkH8aSeD9m2DU0xDXyRjUvuYjeOc8eKWf0TqWe6ji718qZkuKkegGRUGXyyp3jiEPgtUO238wkmGRk1F5aRGRSlPXteo29GHSV39HXP42uq96CM6Z6d/jKdzFRhedxS8YLREA1gDocjkMuMv4drsu6HEtLHkFDm42Yi0tBfxHeYeN7mqHt0NUM7juq5MPRE/oaiRwn4w1qvJ9cIkxx0+fv5x4f68X1n9pdFPL2W9sa38+nDcZopsb6/Yg6HcrnHEL7FkBq941xooc3ARz/mFMmNrhAqNrW4tBR4sreL1GAlWQadw45WeWPC5ZL8g09mk3EpLPrFxRBn9RWoSg57VGklwZ0S2gzw3G2LS5j8DNP/j33wk5MXcxlsJsAPIDIggONOGLIBGRekyJTnULcPBr3/8weOGVtM9eDCvfPfmNbUN2eAf89olRCjqrZAJZe6hxA37mHUa1urrEFmBUxPr4T7DkVaMaW0RS2X0KnUbhgfT1EBZvJDl/3OePwuPh+m/h6/HG5KTf3mMkUyMeL9t6lbHZ6I62Y6GxHt3CaLlpd5KJdS0WSO5rLCMnG4UUVr4H+1cbydL6L43WpADH0YTG6z7972HxFGPyy25XQLerjPFGctTh7UfLh/e9sWrnGvQ3Y3zavl/h96+g88VVDk8amGNal22VHecnIuLHlOjUgNg2vXh63tU8ZP8ffP8gtDgbGrcxO6yaV5AF62fAmo8h9eej20MaQ7/bjBvDkBjTwjut0nlOdi+FBZPhwpeOPucqgI+vgb0rjPFYf54BMa3Kd157EFz6hpE0zH/cKGd9cIsxFsliNbrLLXkFPC5jDM7ZE2DgPcZx5REUYbQO9LkB9q8xEp7fPoXsPcfva3MYBQyCo42uV8FRJT+jjeu38VsjMf3xWWNJ7A5dr4SulxsV5fxd6RiuNsPKf/1PJizWKDG98N8w71GITDZaAQPUPUlKlHRby/YGExFaycqSIiJ+TIlODWgTF8bb7vMYYv2Vs1gP02+BG76vnjLDdY272Jj7Zs1HsGnW0eppWKDVYOh+DXS60KhGVtdZLDD8MXh7hFFyuv94Izlxu+DzvxhjbALD4NovIL5Txc896G/QqK1RQGBrCrw5FIpyjbE+AG1HwqinjBLSlZXYHS54DkY8ZiRsAUFHE5ngqNNfB9dzRqn03z41SiDvX2MsKQ9By3OMVp6OF4AjvPIx1ldFeca/CzCKEFSH/ncaXeEOb4M3zzWuV1IvSD7DSLqTzyjfHD0n4/UaN8vOAxDXsXpiltpzzBw6qrgmIlJxSnRqQESQndjwYO7PuY2fgv9BwN6Vxrf2Qx40O7Tqk7bWaLn57VPITT+6PbaDkdx0vQIim5gXX2U162eMcdn4rVER66r/wYw7jCQuIAiu+bji5YSP1fliY2zPx38yurCBsT7qaWg/qlreAgCBodD63IofZw+GzpcYS+4h+H26cY13Ly0ZOP8DfBtsFFvoeoWRzJa35akivJ66V0lu3RdGN8CoZtB2ePWcMygC/vQpLHwa9iwzbmxTfy7bIhrTuiTp6Wv8jO1wtNtjQRZk7TWS5dLJdbP2Gq15WXshe59RtMJqh3+ma+xVfVM6h44KEYiIVIoSnRrSOjaMJTmNWNH1X5y58j4j0WkzzPiGtr5yFxtzw/z6ARxYd3R7SCPjprf71ZDYo/4Pqh76sJHYbJoJ064wxmRYA4zy1y3Prvr5m/QyihSkPGy08Ay4q/KD2mtSaCNjrFLfm4yxKWs/N8ZdHdpqjAla97kx7qrNuUa3v7YjjWMq68hO43e9ZS4BOxYx0huANWQV9BlX9W5ix/J6IWu38e82MLT8x5QWIehzY/VWB2zaB8Z+arzGoa1GUrl7KexeBhkbjdaew9tgzTRjf0cEhCcaSUxRTvleIzjKSNLqctdROV5pxTVNFioiUilKdGpIm7gwlmw/xEL7WZzZ7SrjBvHLW+C2H+tnt5/8TKP71rb5xrot0GiB6H6NkcA1pG55se2h559h1XslA88tcMl/T14YoDIikuCyN6vvfDUtphWc84DR/W7fKljzCWz42qgOt+EbY7FYjRaH9qOMxKdx21Of05UPuxbDlrnG7/nQFt9TFiAI4OcXjKXlOdB7nNHaVpkxLF4v7F1pDPrf8A0c2WG0cjTpBS3OguYDjdgdYSc+fu9KowufzWH826gJFovxO2vc1qjoBsaN7p6VR5OfPSugMNtYSgVFlUyw28RoRY1oUnY9PKlmWt2k5uWXzKFDKNEhDehvrIhILVGiU0PaxBk3TFvTnXDlf2DXz8bN1eyJcNHLJkdXQYe2wUdXG12t7CHGzO7drmzY3w4PnmhUSXPlwQXPG4PxxbgZb9LbWEY9ZVR52/Sd0QKWthZSlxhLysPQqM3RpCe5n9EKcmibMQ/N1rmw8yejW5Xv3DZjv7bDcDU/h19/+Io+lvVYt883qtHtWGgUtuhxDfQad/oCHx63EUtpIlY6FgqMpMzjOppA/Pis0WqX1AtaDDSSn+QzjyY+y0pac7pcWrVWq4oKjoa2w4wFjFbV9N+NG+CIJkbCXN5WKal/jm3RCVWLjohIRSnRqSGtY40bpG3pTmMyyUv+C++eb3T7aneeMaC7PtjxI3z6Z+MDN6IJXPORMeC9oYtIhL98Z4yBaHWO2dHUTRYLJPU0liEPQuZuo5DBplnGv5tDW+Hnl4wlOMYYj3JkZ9lzhCcZN/FthhnjfUonXnW52B+1F/foh7Hm7odVHxj/d3L2Hz1ni7Oh1/XQcczRFgu3yygaseFr2DgTcjOOvlZgmNEq1/FC4/VyM4wWpZ0/GUvWbmOczJ5l8NPzRuKV1BOa9zfKdUP1FSGoLFsAJHYzNwapPcdMFpqsrmsiIhWmRKeGlLbo7DqcR1Gxh8AWA2Hg3cY8JV/fZZSRLZ0Esq5a+R7MnACeYuMb/Kun+VeJ4aQeZkdQv0Qlwxk3G0tBNmybZ7T2bP7eaIHIP2x0F2t2pjGYv80wiOt0+jFdUc3g3H/AOf9nVKtb+a5REW7nj8YSHG1UgyvIMpKsY+YeISjKaFHqdCG0GlK2C5cjzKhwV9pN7MguI+HZtdg4b2aqUU587wrj+aSe0LQKhShEKsrXohNKN7XoiIhUmBKdGhIf4SDMEYCzsJhdh3JpGx8OQ/5hjHFJ+w3eGm5UW6qLN9MeN8x5CH55xVjvfClc/Gr9KBEtdUNQxNHqbe5io3tYUa7ROlLZMWq2gJKucKOMimK//g9WvW9UGFs69eh+obHGWJ5OFxqtPuUdPxbd3Fh6jjXWM1NhZ0mLz8HNMOxflYtbpLJKy0sTpjE6IiKVoESnhlgsFlrHhrJmTxZb051GohMQCH/6BD68wqha9s5oo5JXaf/7uqAgG7640fjGHGDwg8Yg9PpeSU3MYwswxr1Up8gmMPj/YND9sHWe0bUsKMroxtbszOqpihbVDHo0M8YEiZjAm38EC6q6JiJSWUp0alDruDDW7MliW4bz6MaIJPjLLPjkz8bg6mlXwpgXoFcNVXKqiCM7YdrVkLHBmDPm4teMwdcidZXVBu1GGItIA+PNMxKdIypGICJSKZo9rgaVFiTYmu4s+0RQJIz93BhX4HXD1+Nhwb/NnSBx1xJ441wjyQlLMJIxJTkiIuYp6bqWZ40gNLAa524SEfETSnRqkK/EdIbz+CcDAo1KbGffZ6wvmGwkPG5XLUZYYvU0eP9CyDsECd2MySybaNC1iIhpPG4sBZkAeIOjsKj7sIhIhanrWg0qTXS2pefi8XixWv/wQWWxwNCHjcn9Zt5nDK7O3g9XvlfxAdseN2z7AdZ8ZMxibws0BmFbA079OP8wrPvCOEfHMUbypXk5RETMVZCFBaOV3xYSbXIwIiL1kxKdGtQsJoQAq4V8l5v92QU0iTpJ1bI+NxjziXz+F6Mk7zujYexn5SvlfHg7/PqhkeAcOyFiRZ19v1EVzqpGPhER05V0W3N6gwgPCzE5GBGR+kmJTg2y26y0aBzK1nQnW9OdJ090ANqfB9d/axQnSPsN3hwO134Ose2P37coF37/2mgB2vXT0e1BUca4n5aDjLE/bpcxB4676NSPW5xdtyq/iYj4u/xMwJgsVBXXREQqR4lODWsTG8bWdCfb0p2c0y721Ds37Q03pcD/LjNaat4aAdd8bMw94vXCnuXG7PDrpkNRTslBFmh9rjHpYfvRZSdEFBGR+ql0Dh1vqCquiYhUkhKdGtY6LhTWn6QgwYnEtIIbU+Cjq43E5v2LoO9NxozwBzcf3S+6hZHcdL/GGOMjIiINR0miY8yho8lCRUQqQ4lODfNVXvtjielTCW0M130NX9wEm2bCL68Y2wOCofPFRoLTbIDG04iINFQlic4RdV0TEak0JTo1rE2sUT1te3lbdEoFhsBVH8D8x2DvKmNOm86XQlBEDUQpIiJ1iq/rmhIdEZHKUqJTw1rFGqWaDzqLyMwrIqoiH1hWGwx7pGYCExGRuqu06xqhdAxV1zURkcpQ36caFuoIICnSKBBQoe5rIiLiv/IPA6VjdNSiIyJSGUp0akHr0olDK9p9TUREquzf//43FouFe+65x7etoKCAO++8k0aNGhEWFsZll13GgQMHzAvyj3wtOkp0REQqS4lOLWgdW4mCBCIiUmXLly/nv//9L926dSuz/d577+Wbb77hs88+Y+HChezbt49LL73UpCiP58kzWnRUXlpEpPKU6NSCSlVeExGRKnE6nYwdO5Y33niD6Oho3/asrCzeeustnnvuOc4991x69+7NO++8w88//8wvv/xiYsRHefKMFp1sSzgRQRpOKyJSGfrrWQva+Lqu5ZociYiI/7jzzjs5//zzGTZsGI8//rhv+8qVK3G5XAwbNsy3rUOHDjRr1owlS5Zw5plnHneuwsJCCgsLfevZ2dkAuFwuXC5XhWMrPeZkx1pKuq55HFEUFxdX+PxintNdW6m/dG3rjvJeAyU6taC069ruI3kUuNwE2W0mRyQi0rB9/PHHrFq1iuXLlx/3XFpaGoGBgURFRZXZHh8fT1pa2gnPN3nyZCZNmnTc9jlz5hASElLpOFNSUo7f6PUwpsBIdHI9gcyaNavS5xfznPDaSoOga2u+vLy8cu2nRKcWNA4LJDLYTla+i+0ZuXRK0lw4IiI1Zffu3dx9992kpKQQFBRULeecOHEiEyZM8K1nZ2eTnJzMiBEjiIio+N90l8tFSkoKw4cPx27/Q/nogmysq70ARMQ1ZfTos6oUu9SuU15bqdd0beuO0lb101GiUwssFgutY0NZlZrJtgynEh0RkRq0cuVK0tPT6dWrl2+b2+1m0aJFvPzyy3z//fcUFRWRmZlZplXnwIEDJCQknPCcDocDh8Nx3Ha73V6lG54THu/MASDP6yA8LFw3VPVUVf9tSN2la2u+8v7+VYyglqgggYhI7Rg6dChr165l9erVvqVPnz6MHTvW99hutzNv3jzfMZs2bSI1NZX+/fubGHmJkoprmYSqtLSISBWoRaeWtNFcOiIitSI8PJwuXbqU2RYaGkqjRo1822+88UYmTJhATEwMERER3HXXXfTv3/+EhQhqXUkhgixvmEpLi4hUgRKdWqK5dERE6o7nn38eq9XKZZddRmFhISNHjuTVV181OyxD6WSh3jCiQ9Q9RkSkspTo1JLSFp3tB3Nxe7zYrBaTIxIR8R8LFiwosx4UFMQrr7zCK6+8Yk5Ap1Ka6KDJQkVEqkJjdGpJ0+gQAgOsFBV72Hsk3+xwRESkrsrPBEpbdJToiIhUlhKdWmKzWmjVOBSArRk55Tpmx8Fc7vt0Dbd+sIK8Ik0YJyLiF0rH6BBGTKi6romIVJa6rtWi1nFhbEzLYWu6k3M7xJ90v92H83hp/ha+WLUXt8eYS+G7tWlc1rtpbYUqIiJmKUl0jnjDiFKLjohIpSnRqUVtSgoSbEvPPeHzaVkFvPLDVj5enorLbSQ4iZFB7M8qYO6GA0p0RET8gCfvMFYgE3VdExGpCiU6tah16Vw6fygxfdBZyNQF2/jgl10UFnsAOKtNYyaMaEeA1cKFLy9m4eYMClxuguy2Wo9bRERqjzv3EFYgi1Aig9V1TUSkspTo1KI2x5SY9nq9ZOW7eOPH7byzeCd5RW4A+raI5r4R7TmzVSMAPB4v8REODmQX8sv2QwxuH2da/CIiUvM8eUbXteLAKFXoFBGpAiU6tahVbCgWC2Tlu5j83UY+WppKTqFRZKBb00juG9GeQW0bY7Ec/WCzWi0M7RjPtKWppPx+QImOiEgDZy0wEh2Cos0NRESknlPVtVoUZLfRNDoYgNcXbSensJgOCeG8/ufefHXnQM5pF1smySk1vJNRuGDuhgN4vd5ajVlERGqR14utMAsAS4gSHRGRqlCLTi3r3jSK3YfzaRUbyr3D2nF+10Ssp+ma0L9VI0ICbRzILmTd3my6No2spWhFRKRWFTmxeo2WfntYjMnBiIjUb0p0atmTl3blT/2acUaLGAJs5WtQC7LbGNQ2ltnr00jZcECJjohIQ1VSWrrQayckNNzkYERE6jd1XatlEUF2BrRuXO4kp9Swku5rKb8fqImwRESkLiidQ4cwokNVWlpEpCqU6NQT53aIw2qBDfuz2XMkz+xwRESkJpQkOpleJToiIlWlRKeeiAkNpE9zo7/2vA3pJkcjIiI1Iu8wYMyho8lCRUSqRolOPTKsk1Faeu4GdV8TEWmQjm3RUaIjIlIlSnTqkWEdjXE6v2w/RHaBy+RoRESk2pVJdOwmByMiUr8p0alHWsWG0To2FJfby8JNGWaHIyIi1a000SGUGI3RERGpEiU69cywYyYPFRGRhsVTkuhkecOIUtc1EZEqUaJTzwwv6b72w8Z0XG6PydGIiEh1KnYaxQgyCSNKXddERKpEiU4907NZNI1CA8kuKGb5jsNmhyMiItXInWv8XS+wR2Kv4HxrIiJSlv6K1jM2q4VzOxjV11LUfU1EpGEp6brmcUSZG4eISAOgRKceOnacjtfrNTkaERGpLtaCTONBSLSpcYiINARKdOqhs9s2xhFgZffhfDYfcJodjoiIVAevl4Aio0XHGhJjcjAiIvWfEp16KCQwgLPaNAYg5fc0k6MREZFq4crD5jHmSAsMU6IjIlJVSnTqqdLuaykb0k2OREREqkXJ+Jwir42Q0EiTgxERqf8qlei88sortGjRgqCgIPr168eyZctOuu+7776LxWIpswQFBVU6YDEMLSlIsGZ3JunZBSZHIyIiVVY6hw5hxIRpDh0RkaqqcKLzySefMGHCBP71r3+xatUqunfvzsiRI0lPP3nLQkREBPv37/ctu3btqlLQAnERQXRPjgJg3ka16oiI1HsliU6mJgsVEakWFU50nnvuOW6++Wb+8pe/0KlTJ6ZOnUpISAhvv/32SY+xWCwkJCT4lvj4+CoFLYYRpd3XfleZaRGReq800SGUaCU6IiJVFlCRnYuKili5ciUTJ070bbNarQwbNowlS5ac9Din00nz5s3xeDz06tWLJ598ks6dO590/8LCQgoLC33r2dnZALhcLlwuV0VC9h137M+GYnDbGP7zPfy09SBZufmEBFboctZ7DfW6iq5tXaJrUIt8LTrhRIfaTQ5GRKT+q9Cd8cGDB3G73ce1yMTHx7Nx48YTHtO+fXvefvttunXrRlZWFs888wwDBgxg/fr1NG3a9ITHTJ48mUmTJh23fc6cOYSEhFQk5DJSUlIqfWxd5PVCI4eNQ4UeXvw0hW4x/jmnTkO7rnKUrq358vLyzA7Bf/gSnVCaqkVHRKTKarwJoH///vTv39+3PmDAADp27Mh///tfHnvssRMeM3HiRCZMmOBbz87OJjk5mREjRhAREVHhGFwuFykpKQwfPhy7vWF9S/arZSPvLUklMzSZ0aO7mB1OrWrI19Xf6drWHaUt6lLzvLmHsQCZhBETqkRHRKSqKpToNG7cGJvNxoEDZceEHDhwgISEhHKdw26307NnT7Zu3XrSfRwOBw6H44THVuWmp6rH10UjuyTy3pJUFmw6iNUWgM1qMTukWtcQr6sYdG3Np99/7XE5DxFIaTEC/d5FRKqqQsUIAgMD6d27N/PmzfNt83g8zJs3r0yrzam43W7Wrl1LYmJixSKVE+rbIoaIoAAO5Rbxa+oRs8MREZFKcuUeBiDfFo4jwGZyNCIi9V+Fq65NmDCBN954g/fee48NGzZw++23k5uby1/+8hcArrvuujLFCh599FHmzJnD9u3bWbVqFddeey27du3ipptuqr534cfsNitDSubUSdmg6msiIvWVJ89IdIodUeYGIiLSQFR4jM5VV11FRkYGDz/8MGlpafTo0YPZs2f7ChSkpqZitR7Nn44cOcLNN99MWloa0dHR9O7dm59//plOnTpV37vwc8M6xvPV6n3M/f0AE0d1NDscERGpBEt+JgCeoChT4xARaSgqVYxg/PjxjB8//oTPLViwoMz6888/z/PPP1+Zl5FyOqd9LHabhW0ZuWzPcNIqNszskEREpIKshZkAWEKizQ1ERKSBqHDXNal7IoLsnNmqEQBz1X1NRKReCizKAsAW2sjkSEREGgYlOg3EsI5G18G5v6ebHImIiFSYK58ATwEAjvDGJgcjItIwKNFpIIZ2NAoSrNh1mMO5RSZHIyIiFVIyWWix10pwWKTJwYiINAxKdBqIptEhdEqMwOOF+RvVqiMiUq+UJDqZhBETdvw8ciIiUnFKdBqQYZ1Ku69pnI6ISL1SkuhkeUOJCgk0ORgRkYZBiU4DMrxknM6iLRlk5qn7mohIvXFsi44SHRGRaqFEpwHp0iSCdvFh5BW5efSb380OR0REyqs00fGGERViNzkYEZGGQYlOA2KxWPj3Zd2wWuDLX/cyf6O6sImI1AfevNIWnVCiQ9WiIyJSHZToNDC9mkVz41ktAXjwy3VkF7hMjkhERE7HlXsIgExvuLquiYhUEyU6DdB9I9rTsnEoadkFPPHtBrPDERGR0yjKMRIdpyWM4ECbydGIiDQMSnQaoCC7jacv74bFAp+s2M2izRlmhyQiIqdQ7DQSHZcjytxAREQaECU6DVTfFjFc378FABO/XIuzsNjcgERE5KRKx+gUK9EREak2SnQasAfOa09yTDB7M/OZPEtd2ERE6iprgZHoEBxlahwiIg2JEp0GLCQwgKcu6wbAh0tT+XnrQZMjEhGRE7EVZgFgCYkxORIRkYZDiU4DN6B1Y8b2awbA/335G7nqwiYiUucEuoxExx6qREdEpLoo0fEDE0d3pElUMLsP5/Of7zeZHY6IiByruJBATz4AjvBGJgcjItJwKNHxA2GOACZf2hWAd3/eybIdh02OSEREfPIzAXB7LQSHq0VHRKS6KNHxE4PaxXJVn2QA/u+L38gvcpsckYiIAJBvFCLIIpSYsCCTgxERaTiU6PiRf1zQkYSIIHYczOW5FHVhExGpE0oSnUxvGFEhdpODERFpOJTo+JGIIDtPXtoFgLd+2sGq1CMmRyQiIuQb3YmzCCMmNNDkYEREGg4lOn7m3A7xXNqzCR4v/O2zNRS41IVNRMRUvhadUKJDlOiIiFQXJTp+6OExnYgNd7AtI5cX5m0xOxwREb/mch4CIBN1XRMRqU5KdPxQVEggj19sdGH778JtrNmdaW5AIiJ+rCDbSHRyCCPMEWByNCIiDYf+ovqpkZ0TGNM9iW/W7OOiVxYTG+4gKSqYplHBJEUF0SQqmKSoYJpEB9MkKpjIYDsWi8XssEVEGpyikhadAnuk/s6KiFQjJTp+bNKFndmW7uT3/dlk5BSSkVN40tad0ECbL/G5tl9zhnWKr91gRUQaKE+uUYygODDS5EhERBoWJTp+LCY0kJl/PYvMPBd7M/ON5Ug++0ofZxqPDzqLyC1ysyXdyZZ0J6t2HeHH/zuXyGD1JRcRqbKSYgTuoGiTAxERaViU6Pg5i8VCdGgg0aGBdGly4m8TC1xuX/LzyNfr2ZaRy7uLd3L3sLa1HK2ISMNjLcwEwBKsREdEpDqpGIGcVpDdRqvYMM5uG8s9w9oB8NZP28nKd5kcmYhI/WcvSXSsoTHmBiIi0sAo0ZEKGd01kbZxYWQXFPPu4p1mhyMiUu85XFkA2MOU6IiIVCclOlIhNquFvw41uqypVUdEpIrcLhyePACCIhqbHIyISMOiREcqTK06IiLVpCDT9zA4opF5cYiINEAqRiAVVtqqc9dHv/LWT9sZN7CFKrCJiFRGfiYAWd4QYsKCzI1FpAFyu924XNXT+8TlchEQEEBBQQFut7taziknZrfbsdlsVT6PEh2plNFdE3lx3ha2pDt5Z/EOX5ECEREpP0uBUVo60xtGVEigydGINBxer5e0tDQyMzOr9ZwJCQns3r1bk/vWgqioKBISEqr0u1aiI5VStlVnB38Z2FKtOiIiFVUyh84RwohRoiNSbUqTnLi4OEJCQqolMfF4PDidTsLCwrBaNfqjpni9XvLy8khPTwcgMTGx0udSoiOVplYdEamLJk+ezJdffsnGjRsJDg5mwIABPPXUU7Rv3963T0FBAffddx8ff/wxhYWFjBw5kldffZX4+PhajbU49zABQJY3jJZKdESqhdvt9iU5jRpV39g3j8dDUVERQUFBSnRqWHBwMADp6enExcVVuhubrpJUms1q8U0a+tZPO1SBTUTqhIULF3LnnXfyyy+/kJKSgsvlYsSIEeTm5vr2uffee/nmm2/47LPPWLhwIfv27ePSSy+t9VgLsg4CkEUY4UH67lGkOpSOyQkJCTE5EqmK0utXlTFW+qsqVTK6SyJt49SqIyJ1x+zZs8usv/vuu8TFxbFy5UoGDRpEVlYWb731FtOmTePcc88F4J133qFjx4788ssvnHnmmbUWa5HzEAD5ARFYrerzL1KdNI6mfquO66dER6rEWtKqM36axuqISN2UlWVMyBkTY0zIuXLlSlwuF8OGDfPt06FDB5o1a8aSJUtOmOgUFhZSWFjoW8/OzgaMbxor821j6TFFOUaiUxgQUW2VocRcpddR19M8LpcLr9eLx+PB4/FU23m9Xq/vZ3WeV07M4/Hg9XpxuVzHdV0r7/8vJTpSZWrVEZG6yuPxcM899zBw4EC6dOkCGIOUAwMDiYqKKrNvfHw8aWlpJzzP5MmTmTRp0nHb58yZU6XuMUf276ApkOMOZNasWZU+j9Q9KSkpZofgtwICAkhISMDpdFJUVFTt58/Jyan2c9aEbt26cfvtt3P77bebHUqlFBUVkZ+fz6JFiyguLi7zXF5eXrnOoURHqkytOiJSV915552sW7eOn376qUrnmThxIhMmTPCtZ2dnk5yczIgRI4iIiKjw+VwuFykpKUQHAXkQFJPE6NGjqxSj1A2l13b48OHY7fosNENBQQG7d+8mLCyMoKDqm5/K6/WSk5NDeHh4jXWLO/fcc+nevTvPP/98lc+1fPlyQkND6+1YpYKCAoKDgxk0aNBx17G0Vf10lOhItVCrjojUNePHj+fbb79l0aJFNG3a1Lc9ISGBoqIiMjMzy7TqHDhwgISEhBOey+Fw4HA4jttut9urdDNrLzK61VlDYnRT3MBU9d+GVJ7b7cZisWC1Wqu1Olppd7XSc9eUU53f6/XidrsJCDj9LXxtV5GsblarFYvFcsL/S+X9v6Wqa1ItrFWswObxePl0xW5uem8Fa/dk1USIIuInvF4v48ePZ/r06cyfP5+WLVuWeb53797Y7XbmzZvn27Zp0yZSU1Pp379/rcZamujYwqqvBK6I1E/jxo1j4cKFvPDCC1gsFiwWC++++y4Wi4XvvvuO3r1743A4+Omnn9i2bRsXXXQR8fHxhIWF0bdvX+bOnVvmfC1atGDKlCm+dYvFwptvvskll1xCSEgIbdu25euvvy5XbG63mxtvvJGWLVsSHBxM+/bteeGFF47b7+2336Zz5844HA4SExMZP36877nMzExuvfVW4uPjCQoKokuXLnz77beV+2WVk1p0pNpUtlVnxc7DTPrmd9buNT7w1+7NZOZfz6Zx2PHfnoqInM6dd97JtGnT+OqrrwgPD/eNu4mMjCQ4OJjIyEhuvPFGJkyYQExMDBEREdx1113079+/ViuuAQQVG90vAsOV6IjUJK/XS77LXaVzeDwe8ovcBBQVV6hFJ9huK1dXtxdeeIHNmzfTpUsXHn30UQDWr18PwN///neeeeYZWrVqRXR0NLt372b06NE88cQTOBwO3n//fcaMGcOmTZto1qzZSV9j0qRJPP300/znP//hpZdeYuzYsezatctXrOVU771p06Z89tlnNGrUiJ9//plbbrmFxMRErrzySgBee+01JkyYwL///W9GjRpFVlYWixcv9h0/atQocnJy+N///kfr1q35/fffKz0/Tnkp0ZFqU9GxOnsz8/n3dxv5Zs0+AMIcAUQG29mbmc9fP/qVD27sh03lVkWkgl577TUABg8eXGb7O++8w7hx4wB4/vnnsVqtXHbZZWUmDK1NFq+bEI8TgODwxrX62iL+Jt/lptPD35vy2r8/OpKQwNPfckdGRhIYGEhISIivG+3GjRsBePTRRxk+fLhv35iYGLp37+5bf+yxx5g+fTpff/11mVaUPxo3bhzXXHMNAE8++SQvvvgiy5Yt47zzzjtlbHa7vUxBlpYtW7JkyRI+/fRTX6Lz+OOPc99993H33Xf79uvbty8Ac+fOZdmyZWzYsIF27Ywvwlu1anXa30lVKdGRalWeVp28omKmLtzOfxduo7DYg8UCV/VJ5r4R7cnKL+LClxfz87ZDPJ+ymftHtj/Bq4iInFxpCdhTCQoK4pVXXuGVV16phYhOzO4+WjUoJFItOiJycn369Cmz7nQ6eeSRR5g5cyb79++nuLiY/Px8UlNTT3mebt26+R6HhoYSERFBenp6uWJ45ZVXePvtt0lNTSU/P5+ioiJ69OgBQHp6Ovv27WPo0KEnPHb16tU0bdrUl+TUFiU6Uq1O1arj9Xr5avU+/v3dRtKyCwA4o2UMD1/QiS5NIgGIDXfw78u68dePfuXlH7bSq3kU53ao34PpREROxF5stOZke4OJDq+fVZFE6otgu43fHx1ZpXN4PB5ysnMIjwivcNe1qgoNDS2zfv/995OSksIzzzxDmzZtCA4O5vLLLz9tOe0/DuK3WCzlmhPo448/5v777+fZZ5+lf//+hIeH85///IelS5cCEBwcfMrjT/d8TVGiI9XuRK06q3dnMumb9fyamglA0+hgHhzdkVFdEo7rt3ph9yRW7jzMe0t2ce8na/j2rrNIjtFNgIg0LIHuXACyvGFEhQSaHI1Iw2axWMrVfexUPB4PxYE2QgIDaqzqWmBgIG736ccSLV68mHHjxnHJJZcARgvPzp07aySm0tcbMGAAd9xxh2/btm3bfI/Dw8Np0aIF8+bNY8iQIccd361bN/bs2cPmzZtrtVVHVdek2v2xAtu9n6zm4lcW82tqJiGBNv42sj1zJ5zD6K6JJx2c94/zO9EjOYqsfBd3fLiKgioOIHS5PTw3ZxO3fbCSI7nVP3mYiEhF2VxGopNJKDGhSnRExKiUtnTpUnbu3MnBgwdP2trStm1bvvzyS1avXs2aNWv405/+VK6Wmcpq27YtK1as4Pvvv2fz5s089NBDLF++vMw+jzzyCM8++ywvvvgiW7ZsYdWqVbz00ksAnHPOOQwaNIjLLruMlJQUduzYwXfffcfs2bNrLGZQoiM1xGjVCSOnoJjpv+4F4LJeTfnh/sHcOaQNQadpxg0MsPLK2F5Eh9hZuzeLR7/9vdKxHHQW8ue3lvLi/K3MXp/Gw1+vr/S5RESqi7fI6LqWRZgmWRYRwOiSZrPZ6NSpE7GxsScdc/Pcc88RHR3NgAEDGDNmDCNHjqRXr141Ftett97KpZdeylVXXUW/fv04dOhQmdYdgOuvv54pU6bw6quv0rlzZy644AK2bNnie/6LL76gb9++XHPNNXTq1IkHHnigXK1XVaGua1IjrFYLD57fkZvfW0H35CgevqAT3ZOjKnSOJlHBvHB1T65/ZxnTlqbSp3k0l/ZqevoDj/Hbnkxu+2Al+7IKCA20UVDs4Zs1+zi/awLndUms0LlERKqTpaRFx2mNUIVJEQGgXbt2LFmypMy20mqRx2rRogXz588vs+3OO+8ss/7HrmwnKtSSmZlZrrgcDgfvvPMO77zzTpntkydPLrN+6623cuutt57wHDExMbz99tvler3qohYdqTFD2sexbtJIvrh9QIWTnFKD2sVy91CjG9yD09eyMS273Md+sXIPl09dwr6sAlo1DuWr8QO5dZBRyvCfM9apC5uImMriMlp0CgMiTY5ERKRhUqIjNep0XdTK46/ntmVQu1gKXB5u/98qcgpcp9zf5fbwyNfrue+zNRQVexjaIY4Z4wfSJi6cu4e1pW1cGAedRfxLXdhExEQBJS06RYERJkciIv7utttuIyws7ITLbbfdZnZ4laaua1LnWa0WplzVgwte/JEdB3N54PPfeHXsifuhHnQWcseHq1i24zAAfx3alnuGtsVa0i3EEWDjmSu6c8mri/l6zT5Gd03kvC4JtfZeRERK2d1Gi47HEW1yJCLi7x599FHuv//+Ez4XEVF/v4xRoiP1QkxoIK+M7cWV/13Cd+vSeOunHVx/ZnKZfdbszuS2/61kf1YBYY4AnruyOyM6H5/EdE+O4tZzWvPagm38c8Y6+rWMIVoVj0SklpWWl/YGK9EREXPFxcURFxdndhjVTl3XpN7o2Syaf57fCYB/f7eRlbuO+J77bMVurvjvEvZnFdAqNpQZdw48YZJT6h5fF7ZCHvlGXdhEpPYFlyQ61hAlOiIiNUGJjtQr1/VvzpjuSRR7vNz9yW9kFsKkbzfwt89/o6jYw7CO8cy4cyBt4sJOeR5HgI3/XNEdqwW+Wr2P79en1dI7EBExBHuMrmsBYTEmRyIi0jAp0ZF6xWKx8O9Lu9ImLowDOYU8sdrG/5buBuDeYe14/c+9iQgq33wUPUq6sAH8Y7qqsIlI7Qr1Gi06jvDGJkciItIwKdGReifUEcDUa3sREmijyGMhzBHAm9f14e5hR4sOlNfdQ492YZukLmwiUlu8HsIwEp3gyEYmByMi0jAp0ZF6qU1cOFPH9qBfrIcvbu3HsE7xlTpPkP1oF7YZq/cxR13YRKQ2FGRjxZi8Lywy1uRgREQaJiU6Um/1b9WIP7Xx0Co2tErn6ZEcxS2DjC5sD05fR2aeurCJSA0rMIqpOL1BREWcekyhiEh5tWjRgilTppgdRp2hREcEowpbm9IqbJpIVERqmCfXmOsrkzCiQso3rlBERCpGiY4IRhe2Z6rQhS0rz8X369P4actBit2eGopSRBqK/OyDAGR5Q4kO0TxeIiI1QYmOSIlju7D9Y8apu7B5vV7W7c3ilR+2csXUn+n1eAq3frCSa99aSr8n5/HQjHUs23EYj8dbW+GLSD2Sl2UkOjmWcOw2fRSLCLz++uskJSXh8ZT9wvSiiy7ihhtuYNu2bVx00UXEx8cTFhZG3759mTt3bqVf77nnnqNr166EhoaSnJzMHXfcgdPpLLPP4sWLGTx4MCEhIURHRzNy5EiOHDG63no8Hp5++mnatGmDw+GgWbNmPPHEE5WOpyYEmB2ASF1yz7C2zN1wgK3pTiZ98zvPX9XD91xWvoufthxkwaZ0Fm7OID2nsMyxrWNDOZLn4lBuER/8sosPftlFQkQQF3RL5MIeSXRtEonFUv6qcMVuD5sPOFmzJ5M1uzPZmJZDk+hg+jSPpnfzaDomRugGSaSeKso5BEC+LdzkSET8hNcLrryqncPjMc5RZANrBT5/7SFQjs//K664grvuuosffviBoUOHAnD48GFmz57NrFmzcDqdjB49mieeeAKHw8H777/PmDFj2LRpE82aNavw27Farbz44ou0bNmS7du3c8cdd/DAAw/w6quvArB69WqGDh3KDTfcwAsvvEBAQAA//PADbrcbgIkTJ/LGG2/w/PPPc9ZZZ7F//342btxY4ThqUqUSnVdeeYX//Oc/pKWl0b17d1566SXOOOOMk+7/2Wef8dBDD7Fz507atm3LU089xejRoysdtEhNCbLb+M/l3bjstZ+Z/uteeiRH4SwsZsGmdFalZuI+poUm2G5jYJtGDG4fxzntYkmOCaHY7eHnbYf4es0+vl+XRlp2AW/+tIM3f9pB80YhjOmWxIU9kmgXX/bmxuv1sudIPqt3G0nNmj2ZrN2bRYGr7Lc6q3dnMvO3/SWxWuneNIreJYlPr2bRRIeqC4xIfeByGmN0Cu2RJkci4idcefBkUpVOYQWiKnPgg/sg8PSFk6Kjoxk1ahTTpk3zJTqff/45jRs3ZsiQIVitVrp37+7b/7HHHmP69Ol8/fXXjB8/vsJh3XPPPb7HLVq04PHHH+e2227zJTpPP/00ffr08a0DdO7cGYCcnBxeeOEFXn75Za6//noAWrduzVlnnVXhOGpShROdTz75hAkTJjB16lT69evHlClTGDlyJJs2bSIuLu64/X/++WeuueYaJk+ezAUXXMC0adO4+OKLWbVqFV26dKmWNyFSnXo2i+bmQa3478Lt/OsPhQnaxIUxuF0sg9vH0bdlNI4AW5nnA2xWBrWLZVC7WB6/uAuLNmfwzW/7mfv7AXYdyuPlH7by8g9baR8fzvndEvF68bXYHDrBhKXhjgC6JUfSvWkUHRIjSD2Uy8pdR1iVmklWvoulOw6zdMdh3/6tY0N9iU+P5Ghiwx2EBwWo5aeWOAuL2XsknyN5RcSFO0iKCibIbjv9geJ3PHlG14/iQCU6InLU2LFjufnmm3n11VdxOBx8+OGHXH311VitVpxOJ4888ggzZ85k//79FBcXk5+fT2pqaqVea+7cuUyePJmNGzeSnZ1NcXExBQUF5OXlERISwurVq7niiitOeOyGDRsoLCz0JWR1VYUTneeee46bb76Zv/zlLwBMnTqVmTNn8vbbb/P3v//9uP1feOEFzjvvPP72t78BRvaZkpLCyy+/zNSpU6sYvkjNuHdYO37ZdojNB5wMbNOIc9rHMbik1aa8guw2RnROYETnBPKKipm7IZ1v1uxj4aYMNh3IYVNKTpn97TYLnRIj6J4cRfemUXRPjqJV49ATToLq8XjZftDJyl1HWLnrCCt2HWF7Ri7bSpZPV+wps78jwEp4kJ3woADCgwIIc5T+PLot1BGAI8CKI8BGYIAVR4CVwJLF4VuOPhdgs1IaWWmLvKVky9F1fA8sWLBZLdgsFqxWsFktWC3GYjymQl37apvX6yUr38WeI/nsOZLP3sx89h7JZ8+RPONxZj6Zea7jjmsUGkhSVDBJUUEkRQXTJCq4ZN3Y1jjUUeGJbssbr9vjpdhj/AwJtNXp36+/yfYGkeqJpSC4cnOAiUgF2UOMlpUq8Hg8ZOfkEBEejrWiXdfKacyYMXi9XmbOnEnfvn358ccfef755wG4//77SUlJ4ZlnnqFNmzYEBwdz+eWXU1RU8Wkxdu7cyQUXXMDtt9/OE088QUxMDD/99BM33ngjRUVFhISEEBwcfNLjT/VcXVKhRKeoqIiVK1cyceJE3zar1cqwYcNYsmTJCY9ZsmQJEyZMKLNt5MiRzJgx46SvU1hYSGHh0fEP2dnZALhcLlyu428kTqf0mMocK3VXTV5XG/DZLWfg9VLmJrSyr2W3wKhOsYzqFEt2vos5G9KZvzGDYLuN7smRdGsSQcfECBwBZf9wut3FlHSFPU7z6CCaRydyaY9EAA7nFrF6Txa/pmayKjWT9fuzyS00Di4s9lDoLOSgs/DEJ6sjrJajCZDXY+PvK+b6bs7/mDRB2YTq2GTL99jyh3WMZKp0f4ulJMEq3W4Bq+/50nVwe/j/9u49Nqqq3eP4b3qZ6dS2jKXQllvByEVJwQChKSgaW0BUQMhBgiBy04CiBgwS7qAoaAISCQk5GG4RwkW5/HE8CClUCgoGLPQVhAMELFragi++bYG20846f0BHhrbaG0xn9/tJhplZ+9K1WZ399Jm19trKKyjWjdJqGuMuzZwhejjcrquFJbpRWq4/bpTqjxul+tfv/6ly/dBgm5o/ZFfQXUnI3fmIT2pSscD8lcCU35XQeO5KbO6dB+OXhakKqUPPHufN+2Nf7ET99/+l6LXW7fRf/q4M0BTYbDUaPva3PB4ptPz2fmqT6NRCWFiYhg8frk2bNun8+fPq3LmzevToIen2xADjxo3TsGHDJElFRUW6dOlSnX7O8ePH5fF4tGzZMm/Stm3bNp91unXrprS0NC1atKjS9h07dpTT6VRaWpomTZpUpzo8CLVKdK5du6by8nLFxvp+AxUbG1vtxUe5ublVrp+bW/30vUuWLKnyP3Xv3r0KD695Vnyvffv21XlbNF6B2K7hkl503Xnz78vK+beU86+G2fdjkh6LlxQvlRuppFy6VSYVl1c8bJXeF5dJxR6prOJhJLdHKvPYVGb+Knebv5ZXzKJ977xy5p4Xxvv2n3sTPEbylJs7W9luB5VGJiLUqLlDethhFG2XosOMHnZI0XajaIcUFlImqVjGSLfKpesl0vUSm66X3nkuka6X3n7+T6nkLpdyC+5/Avo//7tHoXWIyzdv1vPiXVSpovePqaUB3Gv06NF68cUXderUKY0ZM8Zb3rFjR+3YsUODBw+WzWbTvHnzKs3QVlOPPvqo3G63Vq5cqcGDB+vw4cOVRlrNmjVLiYmJevPNNzV58mTZ7XYdOHBAI0aMUExMjGbOnKn3339fdrtdffv21dWrV3Xq1ClNnDixXsffkBrlrGuzZs3y6QUqKChQ27ZtNWDAAEVFRdV6f263W/v27VP//v0VGsqN2ayCdg08njs9EN5nc2+ZVO4xKil1KyMjQ08++aSCQ26fpoz3H8nIyFS8NnclU8ZUud5fy3239Zjbrz13tvO+vvNcsY4ktYwMU6tmYXLaG+6aG3e5R/mFJfqjyHfYQaXk0dxbIoUEBSk4yKaQIJuC7jwH3/2wScF31gkOuj15Rl2GrlX0qKNhvdGvg5rfzNaLiXH+rgqARubZZ59VdHS0zp49q1deecVbvnz5ck2YMEF9+vTxJhp1PUd3795dy5cv1yeffKJZs2apX79+WrJkicaOHetdp1OnTtq7d69mz56t3r17y+l0KikpSaNGjZIkzZs3TyEhIZo/f75ycnIUHx+vyZMn1+/gG1itEp2YmBgFBwcrLy/PpzwvL09xcVWfrOPi4mq1viQ5HA45HI5K5aGhofX6g7a+26Nxol2tx+1263SY1KFllKXbNjRUah/mUPsW/q5J9az8/+9PCdHh6uIySmhe91EKAKwpKChIOTmVrydq37699u/f71P21ltv+byvzVC2adOmadq0aT5lr776qs/7p59+WocPH662nnPmzNGcOXNq/DMftFoNZLDb7erZs6fS0tK8ZR6PR2lpaUpOTq5ym+TkZJ/1pdtDjapbHwAAAADqq9YjtqdPn641a9Zow4YN+uWXXzRlyhTduHHDOwvb2LFjfSYrePfdd7Vnzx4tW7ZMZ86c0cKFC3Xs2LE6zfcNAAAA4O9t2rRJERERVT4q7oXTFNT6Gp2RI0fq6tWrmj9/vnJzc/XEE09oz5493gkHsrOzfabc69OnjzZv3qy5c+dq9uzZ6tixo3bt2sU9dAAAAID7YMiQIUpKSqpyWVMaklynyQimTp1abY9Menp6pbIRI0ZUe8MhAAAAAA0nMjJSkZGR/q6G33G7dAAAAACWQ6IDAAAAy6lqan4EjoZoPxIdAAAAWEbFNSjc8DiwVbRffa4papQ3DAUAAADqIjg4WC6XS/n5+ZKk8PDwOt0w+V4ej0elpaUqLi72mXgLDcsYo5s3byo/P18ul0vBwXW/UTeJDgAAACyl4sb0FclOQzDG6NatW3I6nQ2SOOHvuVwubzvWFYkOAAAALMVmsyk+Pl4tW7aU2+1ukH263W4dPHhQ/fr1a1JTNPtDaGhovXpyKpDoAAAAwJKCg4Mb5A/min2VlZUpLCyMRCdAMMAQAAAAgOWQ6AAAAACwHBIdAAAAAJYTENfoVNwwqKCgoE7bu91u3bx5UwUFBYyptBDa1bpo28aj4rzLjfd8EZdQHdrWumjbxqOmsSkgEp3CwkJJUtu2bf1cEwBomgoLC9WsWTN/V6PRIC4BgP/9U2yymQD4ms7j8SgnJ0eRkZF1mre8oKBAbdu21eXLlxUVFXUfagh/oF2ti7ZtPIwxKiwsVKtWrbhB3l2IS6gObWtdtG3jUdPYFBA9OkFBQWrTpk299xMVFcUvpgXRrtZF2zYO9ORURlzCP6FtrYu2bRxqEpv4eg4AAACA5ZDoAAAAALCcJpHoOBwOLViwQA6Hw99VQQOiXa2LtoXV8TtuXbStddG2gScgJiMAAAAAgNpoEj06AAAAAJoWEh0AAAAAlkOiAwAAAMBymlSiY7PZtGvXLn9XAw2Mdm06Ll26JJvNphMnTvi7KkCD4RxmTbRr00Bcatwsl+isWrVK7du3V1hYmJKSkvTjjz/6u0qop4ULF8pms/k8unTp4u9qoQ4OHjyowYMHq1WrVlX+EWCM0fz58xUfHy+n06nU1FSdO3fOP5UFGhCxyXqITdZAXLI2SyU6W7du1fTp07VgwQL99NNP6t69uwYOHKj8/Hx/Vw311LVrV125csX7OHTokL+rhDq4ceOGunfvrlWrVlW5/NNPP9Xnn3+u1atX6+jRo3rooYc0cOBAFRcXP+CaAg2H2GRdxKbAR1yyNkslOsuXL9frr7+u8ePH6/HHH9fq1asVHh6utWvXVrn+ggULFB8fr6ysrAdcU9RWSEiI4uLivI+YmJhq16VdG69BgwZp8eLFGjZsWKVlxhitWLFCc+fO1dChQ9WtWzdt3LhROTk51Q7/KC8v14QJE9SlSxdlZ2ff59oDdUNssi5iU+AjLlmbZRKd0tJSHT9+XKmpqd6yoKAgpaam6ocffvBZ1xijt99+Wxs3blRGRoa6dev2oKuLWjp37pxatWqlRx55RKNHj67y5EG7BraLFy8qNzfX5zPcrFkzJSUlVfoMS1JJSYlGjBihEydOKCMjQ+3atXuQ1QVqhNhkbcQmayMuBb4Qf1egoVy7dk3l5eWKjY31KY+NjdWZM2e878vKyjRmzBhlZmbq0KFDat269YOuKmopKSlJ69evV+fOnXXlyhUtWrRITz31lH7++WdFRkZKol2tIDc3V5Kq/AxXLKtQVFSkF154QSUlJTpw4ICaNWv2wOoJ1AaxybqITdZHXAp8lkl0amratGlyOBw6cuTI33Yxo/EYNGiQ93W3bt2UlJSkhIQEbdu2TRMnTpREuzY1o0aNUps2bbR//345nU5/VweoN85hgYfYhLsRlxonywxdi4mJUXBwsPLy8nzK8/LyFBcX533fv39//f777/r2228fdBXRQFwulzp16qTz5897y2jXwFfxOf2nz7AkPf/888rKyqpy6ADQmBCbmg5ik/UQlwKfZRIdu92unj17Ki0tzVvm8XiUlpam5ORkb9mQIUO0efNmTZo0SVu2bPFHVVFPRUVFunDhguLj471ltGvg69Chg+Li4nw+wwUFBTp69KjPZ1iSpkyZoqVLl2rIkCH67rvvHnRVgRojNjUdxCbrIS5ZgLGQLVu2GIfDYdavX29Onz5t3njjDeNyuUxubq4xxhhJZufOncYYY7Zv327CwsLM9u3b/Vhj1MR7771n0tPTzcWLF83hw4dNamqqiYmJMfn5+cYY2jWQFBYWmszMTJOZmWkkmeXLl5vMzEzz66+/GmOMWbp0qXG5XGb37t0mKyvLDB061HTo0MHcunXLGGPMxYsXjSSTmZlpjDHms88+MxERESYjI8NfhwT8I2KTNRGbrIG4ZG2WSnSMMWblypWmXbt2xm63m969e5sjR454l9190jHGmK1bt5qwsDDz9ddf+6GmqKmRI0ea+Ph4Y7fbTevWrc3IkSPN+fPnvctp18Bx4MABI6nS47XXXjPGGOPxeMy8efNMbGyscTgcJiUlxZw9e9a7/b0BxRhjli1bZiIjI83hw4cf8NEANUdssh5ikzUQl6zNZowxD67/CAAAAADuP8tcowMAAAAAFUh0AAAAAFgOiQ4AAAAAyyHRAQAAAGA5JDoAAAAALIdEBwAAAIDlkOgAAAAAsBwSHQAAAACWQ6IDNJBx48bppZde8nc1AACQRFwCSHQAAAAAWA6JDlBLX331lRITE+V0OtW8eXOlpqZqxowZ2rBhg3bv3i2bzSabzab09HRJ0uXLl/Xyyy/L5XIpOjpaQ4cO1aVLl7z7q/jGbdGiRWrRooWioqI0efJklZaW+ucAAQABhbgEVC3E3xUAAsmVK1c0atQoffrppxo2bJgKCwuVkZGhsWPHKjs7WwUFBVq3bp0kKTo6Wm63WwMHDlRycrIyMjIUEhKixYsX67nnnlNWVpbsdrskKS0tTWFhYUpPT9elS5c0fvx4NW/eXB999JE/DxcA0MgRl4DqkegAtXDlyhWVlZVp+PDhSkhIkCQlJiZKkpxOp0pKShQXF+dd/8svv5TH49EXX3whm80mSVq3bp1cLpfS09M1YMAASZLdbtfatWsVHh6url276oMPPtCMGTP04YcfKiiIjlcAQNWIS0D1+E0FaqF79+5KSUlRYmKiRowYoTVr1uj69evVrn/y5EmdP39ekZGRioiIUEREhKKjo1VcXKwLFy747Dc8PNz7Pjk5WUVFRbp8+fJ9PR4AQGAjLgHVo0cHqIXg4GDt27dP33//vfbu3auVK1dqzpw5Onr0aJXrFxUVqWfPntq0aVOlZS1atLjf1QUAWBxxCageiQ5QSzabTX379lXfvn01f/58JSQkaOfOnbLb7SovL/dZt0ePHtq6datatmypqKioavd58uRJ3bp1S06nU5J05MgRRUREqG3btvf1WAAAgY+4BFSNoWtALRw9elQff/yxjh07puzsbO3YsUNXr17VY489pvbt2ysrK0tnz57VtWvX5Ha7NXr0aMXExGjo0KHKyMjQxYsXlZ6ernfeeUe//fabd7+lpaWaOHGiTp8+rW+++UYLFizQ1KlTGQcNAPhbxCWgevToALUQFRWlgwcPasWKFSooKFBCQoKWLVumQYMGqVevXkpPT1evXr1UVFSkAwcO6JlnntHBgwc1c+ZMDR8+XIWFhWrdurVSUlJ8vklLSUlRx44d1a9fP5WUlGjUqFFauHCh/w4UABAQiEtA9WzGGOPvSgBN2bhx4/Tnn39q165d/q4KAADEJVgG/Y8AAAAALIdEBwAAAIDlMHQNAAAAgOXQowMAAADAckh0AAAAAFgOiQ4AAAAAyyHRAQAAAGA5JDoAAAAALIdEBwAAAIDlkOgAAAAAsBwSHQAAAACWQ6IDAAAAwHL+H1nOeqmfPY3+AAAAAElFTkSuQmCC",
            "text/plain": [
              "<Figure size 1000x500 with 2 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "plot_learning_curves(history, sample_step=500)  #横坐标是 steps"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 24,
      "metadata": {
        "id": "e0JxP6FE9L8Y"
      },
      "outputs": [],
      "source": [
        "a =py7zr.SevenZipFile(r'./test.7z','r')\n",
        "a.extractall(path=r'./competitions/cifar-10/')\n",
        "a.close()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 25,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KSDxPyov9i-y",
        "outputId": "60cc493c-dfb9-495c-ca4a-45488007f0f9"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "300000\n"
          ]
        }
      ],
      "source": [
        "!ls competitions/cifar-10/test|wc -l"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 26,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "H39dl-23-cil",
        "outputId": "de04d73d-186f-488c-e8bf-7734cb96f636"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "competitions   sample_data\t     trainLabels.csv\n",
            "kaggle.json    sampleSubmission.csv  wangdao_deeplearning_train.py\n",
            "model_weights  test.7z\n",
            "__pycache__    train.7z\n"
          ]
        }
      ],
      "source": [
        "!ls"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 27,
      "metadata": {
        "ExecuteTime": {
          "end_time": "2025-06-26T01:45:37.818553Z",
          "start_time": "2025-06-26T01:45:37.816716Z"
        },
        "id": "Yvx48aMb4pNw"
      },
      "outputs": [],
      "source": [
        "# # 导入所需库\n",
        "# import os\n",
        "# import pandas as pd\n",
        "# from PIL import Image\n",
        "# import torch\n",
        "# from torch.utils.data import Dataset, DataLoader\n",
        "# from torchvision import transforms\n",
        "# import tqdm\n",
        "\n",
        "# # 定义测试数据集类\n",
        "# class CIFAR10TestDataset(Dataset):\n",
        "#     def __init__(self, img_dir, transform=None):\n",
        "#         \"\"\"\n",
        "#         初始化测试数据集\n",
        "\n",
        "#         参数:\n",
        "#             img_dir: 测试图片目录\n",
        "#             transform: 图像预处理变换\n",
        "#         \"\"\"\n",
        "#         self.img_dir = img_dir\n",
        "#         self.transform = transform\n",
        "#         self.img_files = [f for f in os.listdir(img_dir) if f.endswith('.png')]\n",
        "\n",
        "#     def __len__(self):\n",
        "#         return len(self.img_files)\n",
        "\n",
        "#     def __getitem__(self, idx):\n",
        "#         img_path = os.path.join(self.img_dir, self.img_files[idx])\n",
        "#         image = Image.open(img_path).convert('RGB')\n",
        "\n",
        "#         if self.transform:\n",
        "#             image = self.transform(image)\n",
        "\n",
        "#         # 提取图像ID（文件名去掉扩展名）\n",
        "#         img_id = int(os.path.splitext(self.img_files[idx])[0])\n",
        "\n",
        "#         return image, img_id\n",
        "\n",
        "# # 定义预测函数\n",
        "# def predict_test_set(model, img_dir, labels_file, device, batch_size=64):\n",
        "#     \"\"\"\n",
        "#     预测测试集并生成提交文件\n",
        "\n",
        "#     参数:\n",
        "#         model: 训练好的模型\n",
        "#         img_dir: 测试图片目录\n",
        "#         labels_file: 提交模板文件路径\n",
        "#         device: 计算设备\n",
        "#         batch_size: 批处理大小\n",
        "#     \"\"\"\n",
        "#     # 图像预处理变换（与训练集相同）\n",
        "#     transform = transforms.Compose([\n",
        "#         transforms.ToTensor(),\n",
        "#         transforms.Normalize((0.4917, 0.4823, 0.4467), (0.2024, 0.1995, 0.2010))\n",
        "#     ])\n",
        "\n",
        "#     # 创建测试数据集和数据加载器\n",
        "#     test_dataset = CIFAR10TestDataset(img_dir, transform=transform)\n",
        "#     test_loader = DataLoader(test_dataset, batch_size=batch_size, shuffle=False, num_workers=4)\n",
        "\n",
        "#     # 设置模型为评估模式\n",
        "#     model.eval()\n",
        "\n",
        "#     # 读取提交模板\n",
        "#     submission_df = pd.read_csv(labels_file)\n",
        "#     predictions = {}\n",
        "\n",
        "#     # 使用tqdm显示进度条\n",
        "#     print(\"正在预测测试集...\")\n",
        "#     with torch.no_grad():\n",
        "#         for images, img_ids in tqdm.tqdm(test_loader, desc=\"预测进度\"):\n",
        "#             images = images.to(device)\n",
        "#             outputs = model(images)\n",
        "#             _, predicted = torch.max(outputs, 1) #取最大的索引，作为预测结果\n",
        "\n",
        "#             # 记录每个图像的预测结果\n",
        "#             for i, img_id in enumerate(img_ids):\n",
        "#                 predictions[img_id.item()] = predicted[i].item() #因为一个批次有多个图像，所以需要predicted[i]\n",
        "\n",
        "#     # 定义类别名称\n",
        "#     class_names = ['airplane', 'automobile', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship', 'truck']\n",
        "\n",
        "#     # 将数值标签转换为类别名称\n",
        "#     labeled_predictions = {img_id: class_names[pred] for img_id, pred in predictions.items()}\n",
        "\n",
        "#     # 直接创建DataFrame\n",
        "#     submission_df = pd.DataFrame({\n",
        "#         'id': list(labeled_predictions.keys()),\n",
        "#         'label': list(labeled_predictions.values())\n",
        "#     })\n",
        "#     # 按id列排序\n",
        "#     submission_df = submission_df.sort_values(by='id')\n",
        "\n",
        "#     # 检查id列是否有重复值\n",
        "#     has_duplicates = submission_df['id'].duplicated().any()\n",
        "#     print(f\"id列是否有重复值: {has_duplicates}\")\n",
        "#     # 保存预测结果\n",
        "#     output_file = 'cifar10_submission.csv'\n",
        "#     submission_df.to_csv(output_file, index=False)\n",
        "#     print(f\"预测完成，结果已保存至 {output_file}\")\n",
        "\n",
        "# # 执行测试集预测\n",
        "# img_dir = r\"competitions/cifar-10/test\"\n",
        "# labels_file = r\"./sampleSubmission.csv\"\n",
        "# predict_test_set(model, img_dir, labels_file, device, batch_size=128)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 28,
      "metadata": {
        "id": "f90sLQwP_o3I"
      },
      "outputs": [],
      "source": [
        "# !head -10 cifar10_submission.csv"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 29,
      "metadata": {
        "id": "R1w9Z1W-AOgY"
      },
      "outputs": [],
      "source": [
        "# !wc -l cifar10_submission.csv"
      ]
    }
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